The European Leaders
23 April 2025
London – Remember the scene in The Amazing Spider-Man where Dr. Curt Connors tests his experimental regeneration formula? Instead of immediately reaching for lab rats, he first runs complex simulations on a computer, visualising the potential effects digitally. This created a narrative that AI can cure diseases.
Once confined to cinematic science fiction, the core idea – leveraging immense computational power to bypass traditional, slower testing methods and revolutionise medical research – is rapidly becoming today’s reality. At the heart of this real-world transformation lies Artificial Intelligence (AI), a technology moving swiftly from theoretical potential into tangible biomedical breakthroughs.
For business leaders, investors, and the public alike, the critical question is no longer if AI will impact medicine, but how profoundly and how soon. The tantalising prospect that AI Can Cure Diseases, perhaps even within the next decade, is fuelling unprecedented research and investment, positioning AI as arguably the most disruptive force in pharmaceutical and biotechnological innovation today.
The Rise of AI in Disease Treatment
One of the most compelling breakthroughs involves AI-driven drug repurposing, where machine learning algorithms identify existing medicines that can be used to treat rare disease conditions that have historically lacked investment due to small patient populations.
In one extraordinary case, an AI model pinpointed a life-saving treatment for Castleman’s disease, a rare and often fatal immune disorder, offering hope where none previously existed.
But this is just the beginning. A scalable AI platform has now been developed to screen over 7,000 rare diseases using FDA-approved drugs, with the potential to impact nearly 300 million people globally.
These diseases are often dubbed “orphan diseases” because pharmaceutical companies have little incentive to develop treatments. AI, with its pattern-seeking prowess, is changing that narrative.
Financial and Clinical Efficiency
For businesses watching the bottom line, the potential savings are eye-watering. Traditional drug development costs upwards of £2 billion and takes over a decade.
AI models, by contrast, can compress this timeline dramatically, predicting drug efficacy and safety profiles before a single clinical trial begins. This means fewer failed drugs, faster innovation, and a surge in productivity for pharmaceutical companies and startups alike.
Moreover, AI is proving vital in tackling neurodegenerative diseases. In the case of Parkinson’s disease, AI combined genomic and pharmaceutical data to identify rasagiline, an existing drug that may slow disease progression.
Similar models are now being applied to Alzheimer’s disease, with a focus on disease-modifying treatments rather than mere symptom control.
Drug Design, Supercomputers, and Generative AI
AI is no longer just analysing data — it’s creating drugs. Firms like Insilico Medicine have leveraged generative AI to design novel molecules for lung fibrosis, cutting traditional design time from four years to just 18 months. Remarkably, only 79 molecules were synthesised during development, compared to the usual 500-plus in conventional research.
Elsewhere, Recursion Pharmaceuticals is harnessing supercomputing power to generate vast experimental datasets, feeding them into AI systems to identify new cancer targets. Clinical trials for some of these AI-discovered compounds, including those for lymphoma, are already underway.
DeepMind’s Vision: Cure All Diseases?
No one is more bullish on this future than Demis Hassabis, CEO of Google DeepMind. Speaking recently, Hassabis declared:
“I think one day, maybe we can cure all disease with the help of AI. Maybe within the next decade. I don’t see why not.”
His optimism isn’t unfounded. DeepMind’s AlphaFold model has decoded over 200 million protein structures in record time — a task that previously took years for a single protein. Understanding these structures is foundational for designing precise therapies, especially for complex diseases like cancer.
The Dual-Use Dilemma: Expertise vs. Bio-Risk
Furthermore, the very power that makes AI a force for good carries a darker potential. Recent studies, shared exclusively with TIME, indicate that advanced AI models like ChatGPT and Claude now outperform Ph.d.-level virologists in complex lab problem-solving.
Whilst this could accelerate beneficial research, experts like Seth Donoughe from SecureBio express nervousness, warning that this capability could lower the barrier for non-experts to potentially design dangerous pathogens. This highlights the urgent need for robust governance and safeguards.
Leading AI labs like OpenAI and xAI have acknowledged these risks and pledged mitigations, including blocking harmful outputs and implementing specific safeguards, but the debate over industry self-regulation versus formal oversight is intensifying, with figures like Tom Inglesby from Johns Hopkins calling for a clearer policy approach.
Clinical Reality Behind AI Can Cure Diseases!
Despite the immense promise, no AI-discovered drug has been fully approved yet. However, over 18 AI-derived candidates are currently in clinical trials — a number growing rapidly. These include treatments for pulmonary fibrosis, cardiovascular disease, and several forms of cancer.
AI is also being trialled in predictive cardiology, with models developed by institutions like the U.S. National Heart, Lung, and Blood Institute. These systems aim to forecast heart disease risks and personalise treatment in ways that human clinicians alone could not.
A Future Written in Code?
From a business standpoint, the writing is on the wall — or rather, in the code. The companies that embrace AI not as an experiment but as a core operational pillar stand to redefine medicine as we know it.
Investors are already lining up, with biotech funds shifting portfolios towards AI-native startups and big pharma acquiring AI labs at record valuations.
But perhaps most profoundly, we are witnessing the slow but steady handover of biological problem-solving from human minds to machines. And in that transition lies both our greatest hope and our greatest responsibility.
If the forecast holds true, then indeed, AI can cure diseases — and it might just do so before the decade is out.
