Innovation Factory Pitching competition: Women entrepreneurs shaping the future

Summary

This discussion centers on a startup pitch competition featuring three women entrepreneurs presenting AI-powered healthcare solutions at an International Telecommunication Union event. The session was moderated by LJ Rich and judged by Cherie Blair, Juan La Vista Ferrez, and Anna Marks, with opening remarks from ITU Secretary-General Doreen Bogdan Martin. The competition showcased innovative AI applications addressing critical healthcare challenges in underserved communities worldwide.

The first presenter, Eva Gubern from Prediction AI (Spain), introduced an AI-powered anesthesia monitoring system designed to reduce surgical complications by 80% and accelerate patient recovery by 25%. Her solution integrates thousands of data points during surgery to provide real-time predictions, helping anesthesiologists optimize anesthesia levels and potentially generate millions in additional hospital revenue. The judges questioned the system’s applicability in low-income settings and its complexity for medical professionals.

Yvonne Baldwin Mushi from Tanzania presented MamaMate, an offline AI-powered digital companion that delivers life-saving guidance to mothers in remote areas without internet connectivity. Her solution addresses the critical need for maternal and child healthcare information in communities where 2.6 billion people remain offline, supporting multiple local languages and low-literacy interfaces. The judges explored its scalability and data integration capabilities with healthcare systems.

The final presenter, Jhuliana Mercado Ontiveros from Peru, demonstrated Weaver, a cloud-based medical imaging platform that enables healthcare professionals to store, visualize, and share medical images globally while using AI to assist in diagnosis. Her solution has already processed 50,000 medical studies and reduced radiographic film usage by 90%, making advanced imaging technology more accessible and environmentally sustainable.

After deliberation, the judges selected MamaMate as the winner based on its potential to impact the largest number of individuals and its innovative approach to accessibility challenges. However, all three startups received offers for year-long mentoring through Cherie Blair’s Foundation for Women and AI expertise support from the AI for Good Lab, demonstrating the exceptional quality of all presented solutions.

Keypoints

## Major Discussion Points:

– **AI-powered healthcare solutions for underserved communities**: Three women entrepreneurs presented innovative AI solutions addressing critical healthcare challenges – Prediction’s AI-powered anesthesia monitoring system, MamaMate’s offline maternal health guidance device, and Weaver’s cloud-based medical imaging platform for cancer detection

– **Accessibility and inclusivity in AI healthcare technology**: Extensive discussion about how these solutions can work in low-income settings, rural areas, and communities without reliable internet or electricity, with emphasis on serving populations that are typically left behind by traditional healthcare technology

– **Regulatory challenges and scalability concerns**: Judges questioned the entrepreneurs about medical device regulations, data privacy, patient confidentiality, and the practical challenges of implementing and scaling AI healthcare solutions across different healthcare systems globally

– **Mentorship and support for women entrepreneurs**: The session highlighted the need for ongoing mentorship, partnerships, and funding to help women-led startups scale their AI solutions, with judges offering concrete support including year-long mentoring programs and AI expertise

– **Competition outcome and recognition**: MamaMate was selected as the winner based on its potential to impact the largest volume of individuals and its innovative approach to accessibility, though all three startups received offers for mentorship and technical support

## Overall Purpose:

This was a startup pitch competition specifically spotlighting women entrepreneurs using AI to tackle pressing healthcare challenges in their communities. The event was part of an “AI for Good” initiative, designed to accelerate progress toward Sustainable Development Goals by supporting innovative healthcare solutions that serve underserved populations.

## Overall Tone:

The discussion maintained a consistently supportive, encouraging, and professional tone throughout. It began with formal introductions and excitement, evolved into focused technical questioning during the pitch sessions, and concluded with genuine enthusiasm and collaborative spirit as judges offered additional support beyond the competition. The atmosphere was notably inclusive and empowering, emphasizing the importance of supporting women entrepreneurs and ensuring AI serves humanity inclusively.

Speakers

**Speakers from the provided list:**

– **LJ Rich** – Host/Moderator of the session

– **Doreen Bogdan Martin** – Secretary-General of the International Telecommunication Union

– **Juan La Vista Ferrez** – Chief Data Scientist of the AI for Good Lab

– **Anna Marks** – Chair of the Global Board of Directors from Deloitte

– **Cherie Blair** – Judge (has a Foundation for Women with global mentoring platform)

– **Eva Gubern** – Chief Commercial Officer at Prediction AI (startup focusing on making surgery safer through AI-powered anesthesia solutions)

– **Yvonne Baldwin Mushi** – Co-founder and CEO of Elevate AI Africa/MamaMate (offline AI-powered solution for maternal and child health)

– **Jhuliana Mercado Ontiveros** – Founder of Weaver (medical imaging software that empowers healthcare professionals)

**Additional speakers:**

None – all speakers mentioned in the transcript are included in the provided speakers names list.

# Comprehensive Report: AI for Good Startup Pitch Competition

## Executive Summary

This report documents a startup pitch competition held at an International Telecommunication Union event, featuring three women entrepreneurs developing AI-powered healthcare solutions. The session was moderated by LJ Rich and featured a distinguished panel of judges including Cherie Blair, Juan La Vista Ferrez, and Anna Marks. MamaMate, an offline AI-powered maternal health solution from Tanzania, was selected as the winner. All three participants received offers for ongoing mentorship and support, highlighting the exceptional quality of the presented solutions and the judges’ commitment to supporting women entrepreneurs in AI healthcare.

## Event Structure and Participants

### Opening and Context

LJ Rich opened the session by introducing this “exclusive session spotlighting top women entrepreneurs” and explaining that the three finalists “were selected from hundreds of startups from around the globe” and “went through a year-long acceleration program.” Rich, who shared that she is “in remission” from a health condition, provided personal context for the importance of healthcare innovation.

Doreen Bogdan Martin, Secretary-General of the International Telecommunication Union, provided opening remarks introducing the three women entrepreneurs and establishing the context within the broader “AI for Good” initiative.

### Competition Format

The event followed what Rich described as a “battle of the band style competition” format, with each startup given exactly 2 minutes to pitch followed by 3 minutes of questions from the judges. Rich noted this was like “a rather lovely kind of nice version of the Hunger Games” and emphasized the audience was “full of influential, powerful, amazing, interested people.”

### Judging Panel

The competition featured three distinguished judges:

– **Cherie Blair**: Judge with a Foundation for Women featuring a global mentoring platform

– **Juan La Vista Ferrez**: Chief Data Scientist of the AI for Good Lab

– **Anna Marks**: Chair of the Global Board of Directors from Deloitte

## Startup Presentations

### PredictAI: AI-Powered Anesthesia Monitoring

Eva Gubern from Spain presented PredictAI, an AI-powered anesthesia monitoring system. She opened with striking statistics: “3.5 trillion euros are annually spent on surgeries,” “60 million patients suffer anesthesia complications every year,” with “annual cost 30 billion on anesthesia complications.”

**Key Claims:**

– Reduces surgical complications by 80%

– Accelerates patient recovery by 25%

– Only 2% of FDA-approved AI solutions focus on surgeries compared to 80% on diagnostics

– 90% of medical data is currently deleted during surgeries

– Generates potential millions in additional hospital revenue

Eva demonstrated “the medical device that we use in our hospital in Barcelona” and requested “10 million euros to validate the results across hospitals around the world.”

### MamaMate: Offline Maternal Health AI

Yvonne Baldwin Mushi from Tanzania presented MamaMate, beginning with the question: “What does digital inclusivity and artificial intelligence mean in a world where 2.6 billion people are completely offline?” She provided additional context: “over 400 million adults do not own a smartphone” and “over 800 women and 7,000 newborn babies die from preventable complications.”

**Key Features:**

– Functions completely offline without internet connectivity

– Supports multiple local languages including Zulu and Swahili

– Features low-literacy audio interface

– Addresses maternal and child healthcare information needs

– Serves communities where traditional digital solutions cannot reach

Yvonne noted a key challenge: “If we could get AI chips to cost a bit less, that would mean our price could go much lower.”

### Weaver: Medical Imaging Platform

Jhuliana Mercado Ontiveros from Peru demonstrated Weaver, sharing that her aunt “had to wait over a month just to get a CT scan,” which inspired her solution.

**Key Achievements:**

– Processed “50,000 medical studies” to date

– “Reduced 90% uses the radiographic films”

– Reduces costs by 50% compared to traditional solutions

– Enables global collaboration in medical imaging

– Provides AI-assisted diagnostic capabilities

## Judge Interactions and Questions

### Accessibility Challenges

Cherie Blair posed a critical question to Eva: “What about in low-income healthcare settings? Because what you’re saying to me sounds like it’d be fantastic in my country, but in the middle of Africa, how is that going to work?”

Eva responded by drawing on her experience in Rwanda, explaining that PredictAI could train non-anesthesiologists to use the device in settings with limited medical staff.

### Technical Validation

Juan La Vista Ferrez focused on scientific rigor, inquiring about the methodology behind clinical outcome statistics and the importance of randomized control trials for validating AI healthcare claims.

### Regulatory and Privacy

The judges explored regulatory compliance and patient data privacy concerns, particularly regarding Weaver’s cross-border medical image sharing capabilities. Jhuliana addressed these concerns by explaining her platform’s encryption protocols and regulatory frameworks.

## Competition Outcome

### Winner Selection

After deliberating for 4-5 minutes while the startups returned to the stage, the judges selected MamaMate as the winner. Anna Marks explained the decision: “Based on the volume of individuals who can benefit from this particular idea, but also on one of the key themes that have come out through the course of today, which is around how do we get around the issue of accessibility.”

The judges described the decision as difficult, with all expressing that they were “struggling” to choose among the high-quality solutions.

### Universal Support Offered

Despite the competitive format, all three startups received offers for ongoing support:

– Year-long mentoring through Cherie Blair’s Foundation for Women’s global platform

– AI expertise and technical support from Juan La Vista Ferrez through the AI for Good Lab

– Continued networking and collaboration opportunities through Anna Marks and Deloitte

## Key Themes and Discussions

### Digital Inclusion and Accessibility

A central theme throughout the session was ensuring AI healthcare solutions can reach underserved populations. Each entrepreneur addressed this challenge differently:

– Eva focused on training non-medical staff to use sophisticated equipment in resource-limited settings

– Yvonne emphasized completely offline solutions for the most remote areas

– Jhuliana advocated for reduced-cost cloud-based solutions with minimal infrastructure requirements

### Local Adaptation

All presenters emphasized the importance of culturally appropriate solutions, with Yvonne specifically highlighting multi-language support and Jhuliana addressing regional healthcare infrastructure challenges.

### Hardware and Cost Barriers

Yvonne’s comment about AI chip costs highlighted ongoing challenges in making advanced AI solutions truly affordable for global deployment.

## Conclusion

This startup pitch competition successfully showcased innovative AI-powered healthcare solutions developed by women entrepreneurs addressing critical global challenges. The selection of MamaMate as the winner reflected the judges’ prioritization of accessibility and potential impact on underserved populations.

The judges’ decision to offer mentorship and support to all participants demonstrated the exceptional quality of the presented solutions and their commitment to nurturing innovation beyond the competition itself. The session emphasized that effective AI healthcare solutions must address not only technical challenges but also issues of accessibility, affordability, and cultural adaptation.

The competition highlighted the significant contributions of women entrepreneurs in developing AI solutions for underserved communities, with each presenter bringing unique perspectives to addressing global healthcare challenges through artificial intelligence.

LJ Rich: Saira Osama, Jhuliana Mercado Ontiveros, Yvonne Baldwin Mushi, Saira Osama, Saira Osama, Saira So we want to give them a bit of a leg up Hands up. I don’t know how to say that but yes, so we’re doing this We’re going to be doing an exclusive session spotlighting top women entrepreneurs And I think we’re going to give the floor to you Doreen Are we going to sit down? Should we sit okay? Well in that case should I maybe introduce our extra judge that we have here? We’ve got the chief data scientist of the AI for good lab who is Juan La Vista Ferrez Could you please come up and join us as well? And then our judges are complete Are you a good man? Are you a good man? All right, well now it’s time for me to give the floor back to Doreen Bogdan Martin secretary-general of the International Telecommunication Union

Doreen Bogdan Martin: Thank you so much this is The exciting part if I can say it like that really happy to To be part of this session. I want to welcome our finalists who are You’re going to be meeting very very shortly I’m excited to see the innovation factory spotlight this year Incredible women entrepreneurs who are using AI To tackle some of the most pressing health care challenges in their communities from Peru the biomedical engineer Juliana Mercado on diverse She brings us weaver Which is a cloud-based medical imaging platform that uses AI To detect cancer early which then reduces the burden on busy radiologists We also have from Spain Ava Gouberne She is leading prediction AI Designed to help medical teams to be able to make informed decisions about when where and how health care is delivered and Then we have from Tanzania we have Yvonne Baldwin Mushi She’s here to present mama mates, which is an offline digital companion that delivers life-saving guidance to new mothers even in the most remote villages in multiple languages these finalists all amazing They were selected from hundreds of startups from around the globe They went through a year-long acceleration program and they’re seeking mentorship and Sherry may have something to say I won’t Surprise, but you’ll have something great to say afterwards Mentorship, they’re seeking mentorship They’re seeking partnerships and they’re seeking the chance to be able to scale their solutions so that we can accelerate SDG progress. I hope some of the investors in the room and we do have some investors here I hope you’ll be listening carefully to these brilliant ideas by daring Daring to innovate where the need is the greatest these entrepreneurs remind us that Artificial intelligence shines brightest when it actually responds to local needs and when it leaves No one behind These three individuals and their work I think are living proof about the generation that I spoke of this morning The generation that is determined to leverage as Sherry was also just saying to leverage artificial intelligence for good so with that ladies and gentlemen Let the pitches begin and best of luck to each of you. Thank you Thank you, thank you so much and I should also add thank you Doreen that was a brilliant opening I

LJ Rich: Obviously forgot to introduce you. It’s Anna marks the chair of the global board of directors from Deloitte. So a really important person Thank you so much a round of applause both for Anna and for Doreen everybody. Thank you. Thank you very much And in a rather lovely kind of nice version of the Hunger Games Let the pitches begin and I would like to say thank you to all of our judges for taking the time to be here It’s going to be brilliant and it’s lovely to have such an amazing judging panel today I do hope that you’re going to argue politely with each other in the background After our amazing people have given us a pitch because we want this battle of the band style competition between three Fantastic entrepreneurial finalists and also three fantastic judges might I say all very stylish as well So what are we going to do? Well, this is about success of human beings Empowered by AI and today’s startups are showcasing these success stories from the AI for good community So I think it might be time to invite our very first person of the day. It’s exciting, isn’t it? Okay, so this is predict the on and representing them is ever goober the chief commercial officer And let’s invite her up to the stage and then we’re going to give her two minutes to pitch and then three minutes for questions Which I will guide her through so ever, please. Will you join us and stand in the center? You Okay, I’m just gonna check you okay, yeah, are you ready? Are you ready? Okay, we’re gonna have a two-minute timer now the two minutes start now Hi, I’m ever a prediction. We’re making surgery safer 3.5 trillion euros are annually spent on surgeries. They represent 50% of hospital expenditure

Eva Gubern: Safe and efficient surgeries are critical without them. Our hospitals will be in financial trouble unfortunately, many are Despite the high stakes only 2% of AI FDA approved solutions focus on surgeries versus 80% on Diagnostics, what do all surgeries have in common? anesthesia 60 million patients suffer anesthesia complications every year During your surgery your anesthesiologist like our founder Pedro will spend hours Mentally integrating thousands of data points data tracking your heart your lungs your brain Essentially, he’s walking a fine line to little anesthesia. You will be in pain too much anesthesia You could suffer brain damage and the annual cost 30 billion on anesthesia complications the solution the prediction PDA AI personalized anesthesia This is not this. This is the medical device that we use in our hospital in Barcelona It integrates thousands of data points during your surgery and it provides the anesthesiologist with predictions So they can adapt your level of anesthesia before you suffer complications what it means to you as a patient 80% less complications 25% faster recovery what it means to a hospital you can increase surgeries by 25% Unlocking up to 5 million euros in additional revenue every year the ask 10 million euros to validate the results across hospitals around the world and to publish a Global framework on how to bring AI for good into the operating room for prediction anesthesia is just the beginning Thank you

LJ Rich: Well, congratulations you managed to get to the end as well so now we have a three minute timer for judges questions Judges, I’ll just check before I start. You have some questions ready?

Cherie Blair: I’ll ask a question. Excellent. The time then starts now. What about in low-income healthcare settings? Because what you’re saying to me sounds like it’d be fantastic in my country, but in the middle of Africa, how is that going to work?

Eva Gubern: Yes, thanks for your question. I spent three years living in Rwanda, so it’s a very heart-to-my-heart question. We do need medical devices available. In most anesthesias, the minimum standard would be an infusion pump, yes, because you cannot really infuse anesthesia. Only with the infusion pump, we would be already able to predict. The good thing of our solution is that it provides predictions. So normally, this I’ve seen in Africa, in many operating rooms, there is one or zero anesthesiologists, yes? So we could train non-anesthesiologists to use this device. This is not the intention of our first launch. That has to be clear. We need to simplify the solution so it could be used by non-anesthesiologists.

LJ Rich: Brilliant, thank you. Okay, Juan, you have a question?

Juan La Vista Ferrez: Yes, the numbers that you share are very impressive. Yes. How do you come up with those numbers? Like, are these a randomized control trial?

Eva Gubern: Or how do you compare the contrafactual in this case? The contrafactual is there is no standard of care. The standard of care, as I said, it’s Pedro and the thousands of anesthesiologists that do all the calculations in their head. But how do we come to these numbers? Yes. So we tested our prototype that you just saw in a hospital clinic. A hospital, 40 operating rooms in Barcelona. So this is a very big public hospital. So we have all types of patients and all types of surgeries. And we collected data with the device connected, which means it’s collecting all the data of the patients and then we’re reading the predictions. And we could see the difference between we read the information of the devices and we read our predictions. And the delta, the difference, is the impact that we can have. It’s how many complications we can reduce,

LJ Rich: how much money we can save to a hospital. Brilliant. Thank you.

Anna Marks: Ana, do you have a question? Practical question, perhaps. And thank you for the presentation. How complex is this?

Eva Gubern: We’ve talked a lot about the partnering between AI and people. For the anesthetologists to use this, how does that partnering work between the AI and the anesthetist? Thank you. We have already tested the device, obviously, with multiple anesthesiologists. They’re normally used to see five, six, seven devices with thousands of data points. So basically, it would be to make a simple analogy. Instead of Google Maps, yes, and it’s telling you which way to take, this would be the PDA. Imagine you have a paper map, then a weather map, then… So in summary, it’s simplifying their job. They’re used to mentally do all these integrations. So we’re taking out the burden of an anesthesiologist of reading thousands of data and doing it only on one device. So the training is very simple. They’re very easy for them to follow the numbers and follow the predictions. Thank you.

LJ Rich: Wow, thank you very much indeed. That’s Eva from Prediction, ladies and gentlemen. I think it’s an incredibly courageous thing to come up and stand on this stage and talk about something that’s so deeply ingrained in the being of someone to do that. And audience, you’re amazingly friendly and kind and generous to allow these incredible people to talk about their story here. So it’s time to move on to our next startup. Also, judges, really good questions. This is great. I’m finding this way too easy at the moment. Surely, surely something will… No, nothing will go wrong. I’ve got you, audience. So let’s move on to our next startup, and that’s Elevate AI Africa. Let’s please welcome Yvonne Baldwin-Mouchie, co-founder and CEO to our very friendly stage. Come on up. I won’t start the timer until you’re ready, OK? I’m sure you’re ready. I know you are. Good? All right, everybody. Let’s have the two minutes starting now. So we asked ourselves… OK, hold on. We’re not going to start the two minutes until the mic works. Do you want to say a quick hello? Yes. OK, good to go. Hello, everyone. All right. OK, your two minutes start really now. All right. I’m going to start the timer. Now. All right. So we asked ourselves a bold question.

Yvonne Baldwin Mushi: What does digital inclusivity and artificial intelligence mean in a world where 2.6 billion people are completely offline? In Africa alone, over 400 million adults do not own a smartphone or access the internet. This gap is deadly in child and maternal health. Every day, over 800 women and 7,000 newborn babies die from preventable complications in childbirth and pregnancy. Every year, over 5 million children under the age of five die from treatable conditions like dehydration, fever, not because solutions do not exist, but because more often than not, they do not reach the mothers who need it most on time. Our answer is MamaMaid, an offline AI-powered solution that can give access to critical medical information and guidance to mothers anytime, anywhere, particularly built for the realities of underserved communities and co-designed with African midwives and verified by doctors and mothers themselves. This device works offline. No electricity, no app, no Wi-Fi. Just one pocket-sized gadget delivering life-saving information to mothers anytime, anywhere, using AI. My name is Yvonne Baldwin-Mushi, and I believe this is what AI can become when it serves humanity inclusively.

LJ Rich: I thank you because I believe no woman should die giving life and no child should have to die from the lack of information or access to the right knowledge. Thank you. Wow. Thank you very much. Yvonne, that was fabulous. We’re going to do a three-minute questions and answers with our judges. And well done. And I’m just going to see, do you have any questions? Who’s going to be first with some questions?

Cherie Blair: I’m very interested in what you were saying. But when you talk about the unmet need, how would this work for an illiterate woman?

Yvonne Baldwin Mushi: Thank you for the question. Well, MamaMaid is built with a low literacy audio interface, meaning it can also serve the women who cannot read. And they can speak to it in local languages. We have tried it in Zulu and Swahili.

LJ Rich: Wow. That’s amazing. OK, Juan, you have a question. It’s good, isn’t it?

Juan La Vista Ferrez: For all the scenarios that you are considering, what would be the most impactful scenario? Which one do you think that has the biggest impact in reducing the chances of someone having a serious complication?

Yvonne Baldwin Mushi: I believe access to the right knowledge. Because these are communities where they’re served by community health workers sometimes. They’re served by doctors very few times. So this tool does not replace a doctor. But it stretches the line of care whereby a child could have died if not, in the sense that if the mother had the right information, let’s say, how to treat a certain fever or dehydration in a certain way, it could stretch that line of care without the need for a doctor immediately and help them save that child’s life before they perish. Wow. OK. Brilliant answer. Brilliant question.

LJ Rich: Thank you very much.

Anna Marks: And again, just a clarification, because this is really quite inspirational. But just a clarification. The data that the tool is using, does that then get fed back into the medical professionals? Or is that purely just transmitted in whichever way appropriate to the mother themselves?

Yvonne Baldwin Mushi: So the data can get fed back. But in many of these settings, it is offline. And well, it serves as a good and a bad thing. It can get updated and sent back to the community health workers in charge of that particular region in order to give them statistics on what the problems can be in those areas. But at the same time, the fact that it’s offline, it’s trained on data. Well, sadly, I can’t go back to my slides. But we had to go through a lot of data, a lot of medical charts, and World Health, baby growth percentile charts, and things like that in order to feed it with the right information and give it exactly what it needs to help a new mother or somebody who’s in an underserved community truly understand what to do. Got it.

LJ Rich: Thank you. Wow. Thank you. Judges, there are 30 more seconds if you want to ask anything, but it looks like this is a natural. Let me ask again, you talked about talking

Yvonne Baldwin Mushi: to some of the mothers. I mean, how much do you involve the local women in the design and delivery of your service? So the whole design of it, the key contributors to it were midwives, African midwives who attend to these women, and we were also lucky and able to include World Health Organization. And that’s time up, I’m afraid. I’m so sorry.

LJ Rich: There was only 30 seconds. You did manage to get a little question and answer in there. Brilliant. Everybody, that’s Yvonne. Thank you so much for Mama Mate. Thank you. Now after our final pitch, we’re going to invite all three of our startup pitchers back up here for a little bit of a chat while our judges decide who is going to be the winner. But before we do that, we have one final startup ready, and that’s going to be Weaver, represented by Jhuliana Mercado Ontiveros, the founder. Are you ready to come up? Jhuliana, everybody.

Jhuliana Mercado Ontiveros: Hi. Okay. You’re all right? You’re good to go? Okay. Your two minutes start now. Okay. Thank you. Good afternoon, everyone. My name is Jhuliana from Weaver. Well, a few years ago, my aunt was diagnosed with cancer. It was a terrifying moment, but what shook me the most was she had to wait over a month just to get a CT scan. Yes, a month just to confirm the diagnosis and begin the treatment. In many regions, healthcare professionals lack the tools and technology they need to make fast, informed decisions. Critical medical data get lost, workflow are slow, and opportunities for early diagnosis are missed. That’s why we create Weaver. Weaver is a medical imaging software that empowers healthcare professionals to make faster and secure diagnostic decisions. With Weaver, medical can store, visualize, and report medical images all through one single entity platform. For example, a doctor in a rural area from Bolivia can share the scan, the study, with other doctors across the world and together review the patient. We are going even further. We have developed AI technology to report and assist to the medical and physician. And now we are going to make a big impact. We are starting 50,000 medical studies and also we reduced 90% uses the radiographic films. But most importantly, Weaver works where it’s needed most because what matters is what’s inside. Thank you. Wow, that was brilliantly delivered and to time as well. Okay, judges, you know the rules by

Cherie Blair: now. You have three minutes. Who would like to ask the first question? It’s going to be you. Well, let me ask you too. I can see how this works in high-income countries, but what’s its

Jhuliana Mercado Ontiveros: potential in low-income countries? The potential for our solution is because the hospitals don’t need a technological infrastructure. This is a first part for having these advanced solutions. And the other, we are 50% more accessible than traditional image solution. So, we have two impacts. Environmental impact because the hospitals don’t need to use radiographic film because we are storage this data that it’s also important to advance in AI technology because we know the data is more important to reduce the biases. And in the other hand, we reduce the cost, the actual cost that hospitals need to use this type of technology. So, we create both impacts, environmental impact and economic impacts. So, our solution is accessible for rural hospitals and we can provide not only the storage, visualize and report tools, also we can provide AI for improve the reports and accuracy and efficiency. Thank you. Thank you very much. Okay, Juan, you have a question?

Juan La Vista Ferrez: I really believe in this scenario. I actually had, 20 years ago, my nephew was born with a disease and I was the person that actually shared this by email with a doctor to get like a CT scan. So, I definitely think there is a scenario. From a regulatory perspective, would this be considered in like you, I assume that you’re trying to do this in Bolivia, would this be considered a medical device and how

Jhuliana Mercado Ontiveros: do you, if that’s the case, how are you treating the privacy aspect of this platform? Well, all the data is encryption, it’s use with encryption. Also, you have access for use to see the whole system and well, we take account regulatory from Bolivia also and international like IPA. So, we take in account this important aspect but also we want to storage all the medical data that now it’s important from Latin America for improve AI technologies and reduce the biases. Brilliant,

LJ Rich: thank you. Ana, do you have a quick question? Actually, I was going to ask the same question about patient confidentiality and data confidentiality. No, not at all. No, I think this is a really, I can

Anna Marks: see the need for doing this. How scalable do you think it is? How easy is it to roll out? How easy is it to implement this? How easy is it for the practitioners to use it? Yeah, well, the system can

Jhuliana Mercado Ontiveros: access with any device. We have two solutions. One of them is with internet connection. So, the doctor we can access with through the cell phone or the computer and also we have another solution without internet. So, we can share in hospitals with different physicians. And we’re out of time, I’m afraid. But thank you so much. Oh my goodness, it’s so interesting, isn’t it? Thank you. It feels

LJ Rich: really difficult cutting somebody off in mid-flow and basically my job involves giving people a voice. It’s like, no, no, no, it’s time to stop talking now. So, judges, I don’t envy you at all. It’s going to be incredibly difficult to choose a winner. So, whilst you deliberate for maybe four or five minutes, let’s invite our startups back onto the stage and we can have a little chat and give them a round of applause. You can stay here and have a little chat. We’ve only got… Is that okay? Yeah. We can turn your microphones off. Just move. Yeah. Oh, yes. Maybe we turn the mics off. Yes. Goose will cut the microphones. Okay, good. Right. While the judges are chatting to each other, let’s get our startups up and give a nice round of applause to Yvonne, Eva and Juliana. Please, can you come up and join us? Hi. Oh, I wonder if any of you have a microphone. So, yes, we’ve got a mic. We can share that microphone if it works. Come stand here with me a little bit. So, congratulations. How do you all feel now you’ve done that? I’ll start with you, Yvonne, as you’re holding a microphone. Do you feel okay now it’s done? Much better. Yes, everybody does feel much better. And I think it’s one of those really strange experiences, but you did brilliantly, all of you. So, I have just a couple of mini questions to ask you. I think the first one is, what is… And I’m going to start with you. What’s your favorite sort of aha moment so far? What’s your favorite moment of inspiration during your journey? Well, I think we… I believe that this experience, so this inspired to found Weber to help professional healthcare make fast and safer decisions. Oh, lovely. Thank you. And I’m going to ask the same question to you,

Eva Gubern: Eva, and I might borrow your mic if that’s okay. Thank you. Sure. I’ve been 20 years in pharma, working in pharmaceuticals, bringing RNA vaccines or CAR T therapies, and I thought medical devices were straightforward. What a big mistake. So, anyone excited about medical devices, you will understand the complications and regulation on medical devices, and that’s why we’re looking for our supporters so we can bring framework to clarify how AI and medical devices interact in a safe manner. Brilliant. Thank you very much. And what about you? What

Yvonne Baldwin Mushi: was your moment of inspiration? Well, for me, it’s the fact that… We talk a lot about AI, and there’s a lot of people, really, who don’t know what it’s all about. But the fact that… I think we could touch everyone, meaning those people who are in underserved communities, means the most to me.

LJ Rich: Oh, that’s beautifully put. My goodness, I think this is a fantastic area and I feel like this is an opportunity with this audience. It’s full of influential, powerful, amazing, interested people. So the next question I’m going to ask you is, what would you like help with doing next? And I’m going to just talk a little bit more to give you five or ten seconds thinking time. So what would you like help with doing next? And I’m going to once again start with you, please. What would you like help with doing next, if you could do anything?

Jhuliana Mercado Ontiveros: Well, for Weaver, scale our solution and impact more communities that have this problem, this huge and critical problem.

LJ Rich: Okay, brilliant. Thank you. And Yvonne, what about you? What would you like help with?

Yvonne Baldwin Mushi: So for me, if we could get AI chips to cost a bit less, that would mean our price could go much lower. Ah, volume. Okay, thank you for lending me the microphone. What about you, Eva? What would you like?

Eva Gubern: Well, the ask was clear, 10 million euros to validate results across hospitals around the world, yes. And also to publish a global framework on how to bring AI for good into the operating room. My goodness, this is brilliant. Wow, round of applause, I think, for our start-ups.

LJ Rich: Okay, I’m just going to do a quick check on the judges and see how they’re doing. They sound like they’re still arguing, but I’m going to check. Are we close to a solution? Almost there. Almost there. So we’ve got a couple of minutes, everybody. Oh, this is kind of fun. All right, so I’m going to ask you the next question, which is, what are you most curious about to learn next? I might start with you, actually, Eva, as you’ve been standing so well. So what would you be most curious to learn about next? Where’s your expansion? Presume that you’ve scaled. Presume that you’ve got unlimited budget. Presume that you could do anything. What would you explore next? Perfect. The finish of our presentation was anaesthesia is just the beginning.

Eva Gubern: I think if you look into an operating room, there are multiple, multiple medical devices. 90% of the data is deleted after your operation. So imagine if we use that data to predict so many things. How many units of blood do you need? How is your electroencephalogram? There are so many verticals in the operating room. As I said, only 2% of AI FDA approved solutions focus on surgeries, and they’re 50% of the hospital expenditure. So we need to completely upturn this trend.

LJ Rich: Wonderful. Thank you very much. It’s a great question, isn’t it? Wow, and a great answer. So yes, what would you do with curiosity and unlimited resources?

Yvonne Baldwin Mushi: Well, given the fact that at the moment our solution is offline, localized, and can only be updated by the clinical professionals, I would find a way to explore for the low literacy settings, particularly for the mothers using the device, to improve their digital skills even more, like more like leverage the offering of the device. That’s lovely. Thank you very much. And what about you? I know these are brilliant answers, aren’t they?

LJ Rich: Why don’t we just put you lot in charge? Okay, what would you do with unlimited resources and a supportive environment that let you do anything? Where would you be curious to explore next? If you could do anything, what would you do?

Jhuliana Mercado Ontiveros: Well, in Weber, we believe in equity. We assure no one is left behind because where they live or what technology they can access to. So we want to try to provide this technology and AI solution for the people who need it most. That’s wonderful. Well, thank you very much to our incredible startups. Aren’t they amazing, everybody?

LJ Rich: Yes, yes, you are. Okay, I’m going to get a little bit more inquisitive with our judges now, because obviously we’ve got an afternoon of programming to go to. So let me just say hello there, judges. Are we still arguing or have we found consensus? Oh, well, you can’t have 30 minutes. We’ve only got sort of two or three minutes. Almost there. Okay. Wow. Well, my goodness. I think for me, AI and health is a really interesting area. I myself, I’m in remission and I’m very happy to be there. And I’ve been lucky enough to be able to access. Thank you. I’ve been very lucky to access the health care, and I’ve been next to people who have gone on so many health journeys. And I feel really strongly that supporting people and empowering them to take those decisions at those times of need is hard. But the fact that you’re doing such positive work in maternal care, in cancer care, in anesthesiologists, this means the next generation of people are going to benefit greatly from using AI for good. And I really wish you all the very, very best in the future. You’re all destined for great things. What an incredible group of people here on our startup stage. Thank you so much. I’ll tell you what we’re going to do next. We’re going to move you all over to here, because I think this might force the issue so that we can have our winner. And if you just want to stand just here is fine. You don’t have to go too far away. Just come and stand. There we go. That’s quite nice. Oh, it’s like you’ve done this before. And I should also mention that no matter who wins this, who goes on to the next stage, I think all of you are destined for great things. And I’m certain that you are going to be. Yes. Right. I’m certain that people will find you either here in person or on LinkedIn or wherever you are. So congratulations for being on this stage. It’s time for me now to get very firm with our judges. All right, judges, have we chosen somebody who’s going to tell us?

Cherie Blair: Well, we were going to do it this way. OK, good. So we all agree with what you said, how marvelous they are. They really are. And so I want you to say that my Foundation for Women has a global mentoring platform where we take women entrepreneurs and we provide them with a mentor to support them for a year over the Internet. And so I want to offer each of you the opportunity to join that mentoring platform in November.

LJ Rich: Wow. Oh, that’s incredible. So everyone gets a one year mentoring because of that. There’s more. Following that, as part of our AI for Good Lab, we partner with organizations like yours,

Juan La Vista Ferrez: providing AI expertise, working with you to try to help you on these endeavors. We think that the three solutions are amazing and we offer that to you. We offer to have an AI expert on our team working with you guys to do that.

LJ Rich: Wow, that’s unbelievable and amazing. Thank you. On behalf of the start-ups, thank you. Well, thank you for selecting me. Very, very. You could tell how much we were struggling.

Anna Marks: We had the benefit of some pre-read as well. So we had the benefit of looking at the business cases and the thought and the investment of time and energy and emotion that you put into your three organizations. It is hugely impressive. And thank you. I can imagine how scary that is to try to put all the key messages into two minutes. Tough job. You did a brilliant job, which is why we really struggled. Commend all three of them. Conceive the absolute good that your products and your ideas will have. On reflection after much discussion, we felt that based on the volume of individuals who can benefit from this particular idea, but also on one of the key themes that have come out through the course of today, which is around how do we get around the issue of accessibility. We felt we wanted to select MamaMate. Congratulations. Well done. Oh, that’s absolutely incredible. Congratulations.

LJ Rich: Thank you so much, judges. You’ve been absolutely sensational. And MamaMate, you are going on to the next stage. But it sounds like everyone’s going to benefit from some fantastic mentoring from our amazing judges as well. So thank you all for adding that in as well. What an incredible session that was. Thank you so much. Well done. Wow. That’s when you know the people here have chosen such a fantastic collection of startups that everybody wants to help them. And if you want to get involved, then please do get in contact with us. I think that the Innovation Factory has been absolutely excellent. There’s been some friendships that have obviously started backstage and will continue for quite some time. So that’s made me incredibly happy. And we’ve got a lovely show this afternoon, including something I’m looking forward to.

Agreement points

AI healthcare solutions must address accessibility challenges in low-income settings

Speakers

– Eva Gubern
– Yvonne Baldwin Mushi
– Jhuliana Mercado Ontiveros
– Cherie Blair

Arguments

Explaining that PredictAI can train non-anesthesiologists to use the device in settings with limited medical staff

Demonstrating MamaMate’s offline functionality with low-literacy audio interface in local languages for underserved communities

Describing Weaver’s accessibility through reduced infrastructure requirements and 50% lower costs than traditional solutions

Questioning how AI healthcare solutions can work effectively in low-income African healthcare settings

Summary

All speakers acknowledge that AI healthcare solutions must be designed with accessibility in mind, addressing infrastructure limitations, cost barriers, and skill gaps in low-income settings

Topics

Development | Digital access | Capacity development

Need for comprehensive support beyond just technology solutions

Speakers

– Cherie Blair
– Juan La Vista Ferrez
– Anna Marks

Arguments

Offering one-year mentoring through the Foundation for Women’s global platform to all participants

Providing AI expertise and technical support through the AI for Good Lab partnership program

Recognizing the volume of beneficiaries and accessibility focus in selecting MamaMate as the winner

Summary

All judges agree that successful AI healthcare implementation requires ongoing mentorship, technical support, and institutional partnerships beyond initial funding

Topics

Development | Capacity development | Infrastructure

AI should respond to local healthcare needs and leave no one behind

Speakers

– Doreen Bogdan Martin
– Eva Gubern
– Yvonne Baldwin Mushi
– Jhuliana Mercado Ontiveros

Arguments

Introduction of three women entrepreneurs using AI to tackle pressing healthcare challenges globally

Explaining that PredictAI can train non-anesthesiologists to use the device in settings with limited medical staff

Introduction of MamaMate, an offline AI companion delivering life-saving guidance to mothers in remote areas without internet access

Presentation of Weaver, a medical imaging platform enabling global collaboration and AI-assisted diagnostics while reducing costs by 50%

Summary

All entrepreneurs and organizers share the vision that AI healthcare solutions should be designed to address specific local needs while ensuring inclusive access

Topics

Development | Human rights | Digital access

Similar viewpoints

All three entrepreneurs demonstrate understanding that successful AI healthcare solutions must work within existing resource constraints and skill limitations in underserved communities

Speakers

– Eva Gubern
– Yvonne Baldwin Mushi
– Jhuliana Mercado Ontiveros

Arguments

Explaining that PredictAI can train non-anesthesiologists to use the device in settings with limited medical staff

Demonstrating MamaMate’s offline functionality with low-literacy audio interface in local languages for underserved communities

Describing Weaver’s accessibility through reduced infrastructure requirements and 50% lower costs than traditional solutions

Topics

Development | Digital access | Capacity development

Both judges emphasize the importance of technical validation and practical implementation considerations for AI healthcare solutions

Speakers

– Juan La Vista Ferrez
– Anna Marks

Arguments

Inquiring about the methodology behind impressive clinical outcome statistics and randomized control trials

Explaining the partnership between AI and medical professionals in anesthesia monitoring workflows

Topics

Development | Digital standards | Infrastructure

All judges believe in providing comprehensive support to all participants regardless of competition outcome, recognizing the value of all presented solutions

Speakers

– Cherie Blair
– Anna Marks
– Juan La Vista Ferrez

Arguments

Offering one-year mentoring through the Foundation for Women’s global platform to all participants

Recognizing the volume of beneficiaries and accessibility focus in selecting MamaMate as the winner

Providing AI expertise and technical support through the AI for Good Lab partnership program

Topics

Development | Capacity development | Gender rights online

Unexpected consensus

Universal support for all participants despite competition format

Speakers

– Cherie Blair
– Juan La Vista Ferrez
– Anna Marks

Arguments

Offering one-year mentoring through the Foundation for Women’s global platform to all participants

Providing AI expertise and technical support through the AI for Good Lab partnership program

Recognizing the volume of beneficiaries and accessibility focus in selecting MamaMate as the winner

Explanation

Despite being a competition with one winner, all judges spontaneously offered comprehensive support to all participants, showing unexpected consensus that all solutions deserve institutional backing

Topics

Development | Capacity development | Infrastructure

Emphasis on offline and low-infrastructure solutions

Speakers

– Eva Gubern
– Yvonne Baldwin Mushi
– Jhuliana Mercado Ontiveros

Arguments

Explaining that PredictAI can train non-anesthesiologists to use the device in settings with limited medical staff

Introduction of MamaMate, an offline AI companion delivering life-saving guidance to mothers in remote areas without internet access

Describing Weaver’s accessibility through reduced infrastructure requirements and 50% lower costs than traditional solutions

Explanation

All three entrepreneurs independently prioritized offline functionality and minimal infrastructure requirements, showing unexpected consensus on the importance of digital inclusion in AI healthcare

Topics

Development | Digital access | Infrastructure

Overall assessment

Summary

Strong consensus emerged around three main areas: the need for accessible AI healthcare solutions that work in low-resource settings, the importance of comprehensive support beyond technology, and the principle that AI should respond to local needs inclusively

Consensus level

High level of consensus with significant implications for AI healthcare development – all participants agreed that successful solutions must prioritize accessibility, affordability, and local adaptation over technological sophistication alone

Different viewpoints

Implementation approach for low-income healthcare settings

Speakers

– Eva Gubern
– Yvonne Baldwin Mushi
– Jhuliana Mercado Ontiveros

Arguments

Explaining that PredictAI can train non-anesthesiologists to use the device in settings with limited medical staff

Introduction of MamaMate, an offline AI companion delivering life-saving guidance to mothers in remote areas without internet access

Describing Weaver’s accessibility through reduced infrastructure requirements and 50% lower costs than traditional solutions

Summary

Each entrepreneur presents a different approach to addressing healthcare accessibility in low-income settings: Eva focuses on training non-medical staff to use sophisticated equipment, Yvonne emphasizes completely offline solutions for the most remote areas, while Jhuliana advocates for reduced-cost cloud-based solutions that still require some infrastructure

Topics

Development | Digital access | Infrastructure

Unexpected differences

Overall assessment

Summary

The session shows minimal direct disagreement as it follows a pitch competition format rather than a debate structure. The main area of difference lies in technical approaches to healthcare accessibility in low-income settings.

Disagreement level

Very low level of disagreement with minimal implications. The format was collaborative rather than adversarial, with judges providing supportive questioning and all participants receiving mentorship offers. The differences in approach represent complementary solutions rather than conflicting viewpoints, suggesting a consensus on goals with diversity in implementation strategies.

Partial agreements

Similar viewpoints

All three entrepreneurs demonstrate understanding that successful AI healthcare solutions must work within existing resource constraints and skill limitations in underserved communities

Speakers

– Eva Gubern
– Yvonne Baldwin Mushi
– Jhuliana Mercado Ontiveros

Arguments

Explaining that PredictAI can train non-anesthesiologists to use the device in settings with limited medical staff

Demonstrating MamaMate’s offline functionality with low-literacy audio interface in local languages for underserved communities

Describing Weaver’s accessibility through reduced infrastructure requirements and 50% lower costs than traditional solutions

Topics

Development | Digital access | Capacity development

Both judges emphasize the importance of technical validation and practical implementation considerations for AI healthcare solutions

Speakers

– Juan La Vista Ferrez
– Anna Marks

Arguments

Inquiring about the methodology behind impressive clinical outcome statistics and randomized control trials

Explaining the partnership between AI and medical professionals in anesthesia monitoring workflows

Topics

Development | Digital standards | Infrastructure

All judges believe in providing comprehensive support to all participants regardless of competition outcome, recognizing the value of all presented solutions

Speakers

– Cherie Blair
– Anna Marks
– Juan La Vista Ferrez

Arguments

Offering one-year mentoring through the Foundation for Women’s global platform to all participants

Recognizing the volume of beneficiaries and accessibility focus in selecting MamaMate as the winner

Providing AI expertise and technical support through the AI for Good Lab partnership program

Topics

Development | Capacity development | Gender rights online

Key takeaways

Three women entrepreneurs successfully demonstrated AI-powered healthcare solutions addressing critical global health challenges: anesthesia monitoring (PredictAI), maternal health guidance (MamaMate), and medical imaging collaboration (Weaver)

AI healthcare solutions can be adapted for low-income settings through offline functionality, reduced infrastructure requirements, and training non-specialists to use the technology

MamaMate was selected as the winner based on its potential to reach the largest volume of beneficiaries and its focus on accessibility for underserved communities

All three startups demonstrated strong technical validation, regulatory compliance awareness, and clear pathways for scaling their solutions globally

The integration of AI with human expertise in healthcare requires careful consideration of workflow integration, training requirements, and regulatory frameworks

Resolutions and action items

All three participants will receive one-year mentoring through Cherie Blair’s Foundation for Women global mentoring platform starting in November

AI for Good Lab will provide AI expertise and technical support to all three startups through partnership programs

MamaMate (Yvonne Baldwin Mushi) advances to the next stage of the competition as the selected winner

PredictAI seeks $10 million in funding to validate results across hospitals worldwide and publish a global framework for AI in operating rooms

Startups will continue networking and collaboration opportunities established during the event

Unresolved issues

Specific timeline and implementation details for the mentoring and AI expertise support programs were not established

The cost reduction challenge for AI chips mentioned by MamaMate remains unaddressed

Detailed regulatory pathways for medical device approval in different countries were not fully explored

Scalability challenges and specific implementation strategies for each solution in various healthcare systems require further development

The next stage requirements and criteria for MamaMate’s advancement were not clearly defined

Suggested compromises

PredictAI acknowledged that training non-anesthesiologists would require solution simplification, which was not their initial launch intention but could expand accessibility

Weaver offers both internet-connected and offline solutions to accommodate different infrastructure capabilities

All solutions emphasized adaptability to local contexts while maintaining core functionality and safety standards

What about in low-income healthcare settings? Because what you’re saying to me sounds like it’d be fantastic in my country, but in the middle of Africa, how is that going to work?

Speaker

Cherie Blair

Reason

This question immediately challenged the universal applicability of AI healthcare solutions by highlighting the stark reality of resource constraints in different global contexts. It forced a critical examination of whether innovative solutions are truly inclusive or merely serve privileged populations.

Impact

This question fundamentally shifted the discussion from technical capabilities to accessibility and equity. It prompted Eva to reveal her personal experience in Rwanda and forced her to address practical implementation challenges, leading to a more nuanced conversation about training non-anesthesiologists and simplifying solutions for resource-constrained environments.

What does digital inclusivity and artificial intelligence mean in a world where 2.6 billion people are completely offline?

Speaker

Yvonne Baldwin Mushi

Reason

This opening question reframed the entire AI discussion by highlighting a fundamental paradox – how can AI be ‘for good’ if it excludes billions of people? It challenged the assumption that digital solutions are inherently inclusive and forced consideration of offline populations.

Impact

This comment established a new framework for evaluating AI solutions, shifting focus from technological sophistication to true accessibility. It influenced how subsequent questions were framed and likely contributed to the judges’ final decision, as they specifically mentioned ‘accessibility’ as a key criterion for selecting MamaMate.

Despite the high stakes only 2% of AI FDA approved solutions focus on surgeries versus 80% on Diagnostics

Speaker

Eva Gubern

Reason

This statistic revealed a significant gap in AI healthcare applications, highlighting how innovation doesn’t always align with where the greatest impact could be achieved. It challenged assumptions about where AI development is focused versus where it’s most needed.

Impact

This insight added depth to the discussion by revealing systemic imbalances in AI healthcare development. It provided context for why surgical AI solutions are particularly valuable and influenced how the judges and audience understood the significance of her work in an underserved area of healthcare AI.

Based on the volume of individuals who can benefit from this particular idea, but also on one of the key themes that have come out through the course of today, which is around how do we get around the issue of accessibility

Speaker

Anna Marks

Reason

This comment crystallized the judging criteria and revealed how the earlier discussions about accessibility and inclusion had become central to evaluating AI solutions. It showed how the conversation had evolved from technical merit to impact and reach.

Impact

This comment provided closure to the accessibility theme that had run throughout the session, demonstrating how Cherie Blair’s initial challenge about low-income settings and Yvonne’s framing of digital exclusion had fundamentally shaped the evaluation criteria and ultimate decision.

If we could get AI chips to cost a bit less, that would mean our price could go much lower

Speaker

Yvonne Baldwin Mushi

Reason

This seemingly simple comment revealed a crucial barrier to scaling AI solutions for underserved populations – the hardware costs that prevent truly affordable deployment. It highlighted how technical innovation alone isn’t sufficient without addressing economic accessibility.

Impact

This comment shifted the conversation from software capabilities to hardware economics, revealing practical constraints that affect real-world implementation. It demonstrated the interconnected nature of technological, economic, and social challenges in deploying AI for good.

Overall assessment

The key comments fundamentally transformed this from a typical startup pitch session into a deeper examination of equity, accessibility, and true impact in AI healthcare solutions. Cherie Blair’s early challenge about low-income settings established accessibility as a central evaluation criterion, while Yvonne’s framing of digital exclusion provided a philosophical foundation that influenced the entire discussion. The technical insights about surgical AI gaps and hardware cost barriers added practical depth to these equity concerns. These comments created a progression from technical presentations to meaningful dialogue about who benefits from AI innovation and how to ensure solutions reach those who need them most. The judges’ final decision and reasoning directly reflected this evolved framework, showing how thoughtful questioning can elevate a competition beyond mere technical merit to consider broader social impact and true inclusivity.

How can AI solutions work effectively in low-income healthcare settings with limited infrastructure?

Speaker

Cherie Blair

Explanation

This question was raised multiple times regarding the practical implementation of AI healthcare solutions in resource-constrained environments, particularly in Africa and other developing regions where basic medical infrastructure may be lacking.

What methodology and validation processes are used to generate the impressive impact numbers claimed by AI healthcare solutions?

Speaker

Juan La Vista Ferrez

Explanation

This addresses the need for rigorous scientific validation of AI healthcare claims, including randomized control trials and proper counterfactual analysis to ensure the reported benefits are accurate and reproducible.

How can the cost of AI chips be reduced to make AI healthcare solutions more accessible in underserved communities?

Speaker

Yvonne Baldwin Mushi

Explanation

This highlights a critical barrier to scaling AI solutions globally – the hardware costs that prevent widespread deployment in low-resource settings where the need is greatest.

How can a global framework be developed for safely integrating AI into medical devices and operating rooms?

Speaker

Eva Gubern

Explanation

This addresses the regulatory and safety challenges of implementing AI in critical medical settings, which requires standardized approaches to ensure patient safety while enabling innovation.

How can digital skills be improved for low-literacy users of AI healthcare devices?

Speaker

Yvonne Baldwin Mushi

Explanation

This explores the challenge of making AI technology truly accessible to users with limited digital literacy, which is crucial for effective deployment in underserved communities.

How can medical data from underrepresented regions be better utilized to reduce AI bias in healthcare applications?

Speaker

Jhuliana Mercado Ontiveros

Explanation

This addresses the important issue of ensuring AI healthcare solutions are trained on diverse datasets to avoid perpetuating healthcare disparities and improve outcomes for all populations.

How can the vast amount of medical data generated during surgeries (90% currently deleted) be leveraged for predictive healthcare applications?

Speaker

Eva Gubern

Explanation

This represents a significant opportunity for expanding AI applications in healthcare by utilizing currently wasted data to predict various medical needs and outcomes during surgical procedures.