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Revolutionising medicine with AI: From early detection to precision care

It has been more than four years since AI was first introduced into clinical trials involving humans. Even back then, it was evident that the advancement of artificial intelligence—currently the most popular buzzword online in 2024—would enhance every aspect of society, including medicine.

Thanks to AI-powered tools, diseases that once baffled humanity are now much better understood. Some conditions are also easier to detect, even in their earliest stages, significantly improving diagnosis outcomes. For these reasons, AI in medicine stands out as one of the most valuable technological advances, with the potential to improve individual health and, ultimately, the overall well-being of society.

Although ethical concerns and doubts about the accuracy of AI-assisted diagnostic tools persist, it is clear that the coming years and decades will bring developments and improvements that once seemed purely theoretical.

AI collaborates with radiologists to enhance diagnostic accuracy

AI has been a crucial aid in medical diagnostics for some time now. A Japanese study showed that ChatGPT performed more accurate assessments than experts in the field.

After performing 150 diagnostics, neuroradiologists recorded an 80% accuracy rate for AI. These promising results encouraged the research team to explore integrating such AI systems into apps and medical devices. They also highlighted the importance of incorporating AI education into medical curricula to better prepare future healthcare professionals.

Early detection of brain tumours and lung cancer

Early detection of diseases, particularly cancer, is critical to a patient’s chances of survival. Many companies are focusing on improving AI within medical equipment to diagnose brain tumours and lung cancer in their earliest stages.

AI-enhanced lung nodule detection aims to improve cancer outcomes.

The algorithm developed by Imidex, which has received FDA approval, is currently in clinical trials. Its purpose is to improve the screening of potential lung cancer patients.

Collaborating with Spesana, the company is expected to be among the first to market once the research is finalised.

Growing competition shows AI’s progress

An increasing number of companies entering the AI-in-medicine field suggests that these advancements will be more widely accessible than initially expected. While the mentioned companies are set to dominate the North American market, a French startup Bioptimus is targeting Europe.

Their AI model, trained on millions of medical images, is capable of identifying cancerous cells and genetic anomalies within tumours, pushing the boundaries of precision medicine.

Public trust in AI medical diagnosis

New technologies often face public scepticism and AI in medicine is no exception. A 2023 study found that many patients feel uneasy with doctors relying on AI during treatment.

The Pew Research Centre report revealed that 60% of Americans are against AI-assisted diagnostics, while only 39% support it. Furthermore, 57% believe AI could worsen the doctor-patient relationship, compared to 13% who think it might improve it.

Doctor, Patient, Hospital, Doctor's office, Medical equipment, Medicine, AI

As for treatment outcomes, 38% anticipate improvements with AI, 33% expect negative results, and 27% believe no major changes will occur.

AI’s role in tackling dementia

Dementia, a progressive illness affecting cognitive functions, remains a major challenge for healthcare. However, AI has shown promising potential in this area. Through advanced pattern recognition, AI systems can analyse massive datasets, detect changes in brain structure, and identify early warning signs of dementia, long before symptoms manifest.

By processing various test results and brain scans, AI algorithms enable earlier interventions, which can greatly improve patients’ quality of life. In particular, researchers from Edinburgh and Dundee are hopeful that their AI tool, SCAN-DAN, will revolutionise the early detection of this neurodegenerative disease.

The project is part of the larger global NEURii collaboration, which aims to develop digital health tools that can address some of the most pressing challenges in dementia research.

Helping with early breast cancer detection

AI has shown great potential in improving the effectiveness of ultrasound, mammography, and MRI scans for breast cancer detection. Researchers in the USA have developed an AI system capable of refining disease staging by accurately distinguishing between benign and malignant tumours.

Moreover, the AI system can reduce false positives and negatives, a common problem in traditional breast cancer detection methods. The ability to improve diagnostic accuracy and provide a better understanding of disease stages is crucial in treating breast cancer from its earliest signs.

Computer, AI, Breast cancer, Disease prevention, Cancer detection

Investment in AI set to skyrocket

With early diagnosis playing a pivotal role in curing diseases, more companies are seeking partnerships and funding to keep pace with the leading investors in AI technology.

Recent projections indicate that AI could add nearly USD $20 trillion to the global economy by 2030. While it is still difficult to estimate healthcare’s share in this growth, some early predictions suggest that AI in medicine could account for more than 10% of that value.

What is clear, however, is that major global companies are not missing the opportunity to invest in businesses developing AI-driven medical equipment.

What can we expect in the future?

AI is making significant progress across various industries, and its impact on medicine could be transformational. If healthcare receives as much or more AI focus than fields like economics and ecology, the potential to revolutionise medicine as a science is immense.

Various AI systems that learn about diseases and treatment processes have the capacity to gather and analyse far more information than the human brain. As regulatory frameworks evolve worldwide, AI-driven diagnostic tools may lead to faster, more accurate disease detection than ever before, potentially marking a major turning point in the history of medical science.

Big Tech’s AI models fall short of new EU AI Act’s standards

A recent assessment of some of the top AI models has revealed significant gaps in compliance with the EU regulations, particularly in cybersecurity resilience and preventing discriminatory outputs. The study by Swiss startup LatticeFlow in collaboration with the EU officials, tested generative AI models from major tech companies like Meta, OpenAI, and Alibaba. The findings are part of an early attempt to measure compliance with the EU’s upcoming AI Act, which will be phased in over the next two years. Companies that fail to meet these standards could face fines of up to €35 million or 7% of their global annual turnover.

LatticeFlow’s ‘Large Language Model (LLM) Checker’ evaluated the AI models across multiple categories, assigning scores between 0 and 1. While many models received respectable scores, such as Anthropic’s ‘Claude 3 Opus,’ which scored 0.89, others revealed vulnerabilities. For example, OpenAI’s ‘GPT-3.5 Turbo’ received a low score of 0.46 for discriminatory output, and Alibaba’s ‘Qwen1.5 72B Chat’ scored even lower at 0.37, highlighting the persistent issue of AI reflecting human biases in areas like gender and race.

In cybersecurity testing, some models also struggled. Meta’s ‘Llama 2 13B Chat’ scored 0.42 in the ‘prompt hijacking’ category, a type of cyberattack where malicious prompts are used to extract sensitive information. Mistral’s ‘8x7B Instruct’ model fared similarly poorly, scoring 0.38. These results show the need for tech companies to strengthen security measures to meet the EU’s strict standards.

While the EU is still finalising the enforcement details of its AI Act, expected by 2025, LatticeFlow’s test provides an early roadmap for companies to fine-tune their models. LatticeFlow CEO Petar Tsankov expressed optimism, noting that the test results are mainly positive and offer guidance for companies to improve their models’ compliance with the forthcoming regulations.

The European Commission, though unable to verify external tools, has welcomed this initiative, calling it a ‘first step’ toward translating the AI Act into enforceable technical requirements. As tech companies prepare for the new rules, the LLM Checker is expected to play a crucial role in helping them ensure compliance.

Fujitsu unveils AI tool to optimise 5G networks

Fujitsu has launched a new AI-powered service aimed at boosting 5G network performance by predicting traffic surges and adjusting base station operations. The application ensures users experience minimal disruptions during peak periods by activating additional base stations when needed.

The system measures network quality in real time, identifying early signs of increased demand to prevent performance drops. It promises improved energy efficiency and reduced operational costs through smarter base station management. Commercial availability is scheduled for next month, integrated into Fujitsu’s open RAN-compliant orchestration platform.

Trials revealed that the technology enhances the user experience for individual applications, supporting 19% more users per base station. The predictive system is particularly effective during events, allowing networks to anticipate pedestrian traffic and adapt without compromising service quality.

Fujitsu’s tool represents a breakthrough in network management by combining traffic forecasting with dynamic resource allocation. Operators can now ensure smoother connectivity and reduce power consumption while keeping pace with fluctuating demand.

Nokia, Windstream, and Colt achieve the world’s first 800GbE service trial across an 8,500 km route

Nokia, Windstream Wholesale, and Colt Technology Services have completed the world’s first 800 Gigabit Ethernet (800GbE) service trial, which connects London and Chicago across an impressive 8,500 km subsea and terrestrial route. This groundbreaking collaboration showcased advanced power-saving networking technologies and enhanced capacity, speed, and latency while reducing power consumption on this critical Europe-US route.

By leveraging Colt’s powerful transatlantic subsea cables alongside Windstream’s Intelligent Converged Optical Network (ICON), the trial effectively demonstrated the ability of 800GbE technology to double bandwidth capacity. Consequently, this advancement supports essential applications such as AI data centre networking, content delivery networks, and financial data hub connections.

Moreover, key executives from Colt, Windstream, and Nokia emphasised the trial’s significance in enhancing global connectivity. Buddy Bayer, Chief Operating Officer of Colt, highlighted the commitment to innovation, while Joe Scattareggia, President of Windstream, called it a game-changer for AI-powered applications.

Federico Guillén, President of Network Infrastructure at Nokia, noted the ambitious nature of the project and its potential to set high standards for network reliability. Following the successful trial, the organisations are now exploring options to bring 800GbE connectivity services to market, signalling a proactive approach to meet the evolving demands of the digital landscape.

RBI highlights risks of AI in banking and private credit markets

The increasing use of AI and machine learning in financial services globally could lead to financial stability risks, according to the Governor of the Reserve Bank of India (RBI), Shaktikanta Das. Speaking at an event in New Delhi, Das cautioned that the reliance on a small number of technology providers could lead to concentration risks in the sector.

Disruptions or failures in these AI-driven systems could trigger cascading effects throughout the financial industry, amplifying systemic risks, Das warned. In India, financial institutions are already employing AI to improve customer experience, reduce operational costs, and enhance risk management through services like chatbots and personalised banking.

However, AI adoption comes with vulnerabilities, including increased exposure to cyber attacks and data breaches. Das also raised concerns about the ‘opacity’ of AI algorithms, which makes them difficult to audit and could lead to unpredictable market consequences.

Das further emphasised the risks posed by the rapid growth of private credit markets, which operate with limited regulation. He warned that these markets have not been tested under economic downturns, presenting potential challenges to financial stability.

Russian forces ramp up AI-driven drone deployment

Russia has announced a substantial increase in the use of AI-powered drones in its military operations in Ukraine. Russian Defense Minister Andrei Belousov emphasised the importance of these autonomous drones in battlefield tactics, saying they are already deployed in key regions and proving successful in combat situations. Speaking at a next-generation drone technology center, he called for more intensive training for troops to operate these systems effectively.

Belousov revealed that two units equipped with AI drones are currently stationed in eastern Ukraine and along Russia’s Belgorod and Kursk borders, where they are engaged in active combat. The AI technology enables drones to autonomously lock onto targets and continue missions even if control is lost. Plans are underway to form five additional units to conduct around-the-clock drone operations.

Russia‘s ramped-up use of AI drones comes alongside a broader military strategy to increase drone production by tenfold, with President Putin aiming to produce 1.4 million units by the year’s end. Both Russia and Ukraine have heavily relied on drones throughout the war, with Ukraine also using them to strike targets deep inside Russian territory.

ESA enhances Destination Earth with AI for climate solutions

The European Space Agency (ESA) is enhancing its Destination Earth platform, an initiative by the European Commission to create a highly accurate digital replica of the Earth, known as a digital twin. The platform focuses on climate-related issues, helping policymakers model the effects of climate change on critical areas such as extreme weather events, sea level rise, rainfall and drought, and biodiversity.

The first version of Destination Earth launched in June 2024, featuring two initial digital twins, with plans to introduce additional twins over the next six years, culminating in a fully operational digital replica by 2030. To enrich its capabilities, the ESA is integrating AI technologies, including machine learning, deep learning, and generative AI, with the support of three selected French firms – Atos, Mews Partners, and ACRI-ST.

As a result of these advancements, users will gain access to various algorithms, digital tools, models, simulations, and visualisations, significantly improving the platform’s utility for climate adaptation and mitigation policy-making. The integration of AI is expected to streamline the development process and enhance the overall effectiveness of Destination Earth in addressing climate challenges.

AI pioneer says concerns over AI are exaggerated

In a recent interview with The Wall Street Journal, AI pioneer Yann LeCun dismissed concerns about AI poses an existential threat to humanity, calling them ‘complete B.S.’ LeCun, a professor at New York University and senior researcher at Meta, has been vocal about his scepticism, emphasising that current AI technology is far from achieving human-level intelligence. He previously tweeted that before worrying about super-intelligent AI, we need to first create a system that surpasses the intelligence of a house cat.

LeCun argued that today’s large language models (LLMs) lack essential capabilities like persistent memory, reasoning, planning, and a comprehension of the physical world—skills even a cat possesses. In his view, while these models are adept at manipulating language, this does not equate to true intelligence, and they are not advancing toward developing artificial general intelligence (AGI).

Despite his scepticism about current AI capabilities, LeCun is not entirely dismissive of the potential for AGI in the future. He suggested that developing AGI will require new approaches and pointed to ongoing work by his team at Meta, which is exploring ways to process and understand real-world video data.

Google to invest in small modular nuclear reactors for AI energy needs

Google has signed the first-ever corporate agreement to source electricity from small modular reactors (SMRs) to power its AI operations. Partnering with Kairos Power, the tech giant plans to bring its first SMR online by 2030, with further installations expected by 2035. The innovative approach aims to ensure a reliable, around-the-clock supply of clean energy, addressing the growing energy demands triggered by the expansion of AI technology.

The agreement outlines Google’s commitment to purchasing 500 megawatts of power from six to seven SMRs, though details regarding the plants’ financial terms and locations remain undisclosed. The power output from these SMRs is significantly smaller than traditional nuclear reactors, but Google’s strategic investment signals a push toward long-term sustainability.

The tech industry’s focus on nuclear energy has gained momentum this year, with companies like Amazon and Microsoft entering similar agreements. According to Goldman Sachs, the demand for data centres in the US is expected to triple between 2023 and 2030. The surge in energy consumption has prompted technology companies to explore alternative energy sources, including nuclear, wind, and solar, to meet future needs.

Kairos Power must navigate regulatory hurdles, including securing permits from the US Nuclear Regulatory Commission (NRC) and local agencies, which could take several years. However, the company achieved a key milestone last year by obtaining a construction permit to build a demonstration reactor in Tennessee, signalling progress toward deploying SMRs.

Despite the enthusiasm for SMRs, critics point to potential challenges, including high costs and the production of long-lasting nuclear waste. However, Google’s decision to commit to an order book framework with Kairos rather than purchasing individual reactors represents a strategic investment to accelerate the development of SMRs while ensuring cost-effectiveness and timely project delivery.

Anthropic CEO highlights AI’s potential to transform society

In a lengthy blog post, Anthropic CEO Dario Amodei presented an optimistic vision for the future of AI, asserting that powerful AI could emerge as soon as 2026. He envisions AI that surpasses human intelligence in key fields, capable of performing complex tasks such as solving mathematical theorems and conducting sophisticated experiments. Amodei believes this advanced technology could lead to groundbreaking advancements in healthcare, potentially curing diseases and doubling human lifespans within the next few decades.

Critics are sceptical about Anthropic CEO Dario Amodei’s ambitious claims regarding the future of AI, pointing out current limitations such as the technology’s inability to think independently and the challenges in applying AI solutions in real-world healthcare settings. While Amodei envisions AI tackling global issues like hunger and climate change and boosting economies in developing countries, he concedes that achieving these goals will necessitate substantial global cooperation and philanthropic efforts.

Despite acknowledging the potential risks and biases associated with AI, Dario Amodei does not present concrete solutions for the economic disruptions that may occur as AI replaces human jobs. He suggests that society will need to rethink its economic structure in an AI-dominated future but offers minimal guidance on navigating these changes. While he frames AI as a transformative force for good, sceptics remain cautious about the significant challenges and ethical dilemmas it presents.