AI health tech is booming. The cures are not.

"At Novartis, a team of researchers working on Huntington's disease used generative AI to computationally design 15 million potential compounds for a type of molecule called a molecular glue degrader. From those 15 million candidates, the team synthesised roughly 60 in the laboratory. They arrived at a promising scaffold now moving forward for further optimisation."

"The gap, between what AI can do in a laboratory and what it has actually delivered to patients, is the defining tension of health technology in 2026. The industry speaks in the language of revolution. The evidence speaks in the language of incremental, uncertain, and frequently disappointing progress."

"Traditional drug development takes 10 to 15 years and costs an average of $2.5 billion per successful compound, with approximately 90 per cent of candidates failing in clinical trials. AI can compress early discovery timelines by 30 to 40 per cent and reduce preclinical candidate development from three to four years to as little as 13 to 18 months."

AI has enabled researchers at Novartis to design 15 million potential compounds for Huntington's disease, narrowing it down to 60 synthesized candidates. Despite this achievement, it does not equate to a cure. The gap between AI's capabilities and actual patient outcomes highlights the tension in health technology. While AI can significantly reduce drug development timelines and costs, the industry often overstates its revolutionary impact, leading to incremental and uncertain progress. Additionally, health chatbots pose safety risks as millions seek medical advice online.

Read at TNW | Opinion