AI in Medicine: Separating Silicon Valley Dreams from Scientific Reality (Mohammad FarhanNovember 16, 2025, Fair Observer))
During the COVID-19 pandemic, AI helped identify promising drug candidates and accelerated vaccine development timelines. Large language models are now scanning millions of research papers to identify potential therapeutic connections that would take human researchers years to discover.Meanwhile, in neuroscience, AI is being used to decode brain signals from paralyzed patients, enabling them to control computer cursors and robotic arms with unprecedented precision. Brain-computer interfaces powered by machine learning are translating neural activity into text, giving voice to patients who have lost the ability to speak. Researchers are using AI to map neural circuits with cellular precision and simulate brain networks that were previously too complex to model.
In structural biology, AI has achieved remarkable breakthroughs in protein structure prediction, which have major implications in drug discovery. Google DeepMind’s AlphaFold can now predict how proteins fold with stunning accuracy, solving a puzzle that has stumped scientists for decades. This matters because understanding protein structure is fundamental to developing new treatments for human diseases.
In drug discovery, we’re seeing real progress too. Companies like Exscientia made history with the molecule DSP-1181, the first AI-designed drug to enter human clinical trials for treating obsessive-compulsive disorder. In-silico Medicine became the first company to advance an AI-designed drug for an AI-discovered target into clinical trials — a “double first” where AI handled both target identification and drug design. Others, like Recursion Pharmaceuticals, have used AI to identify new drug targets and advance candidates like REC-1245 (an orally bioavailable molecular degrader of the RNA-binding protein 39) for solid tumors from discovery to pre-clinical testing in just 18 months, less than half the typical timeline.