Medical Scientists, Except Epidemiologists

AI is increasingly used to help medical scientists rather than replace them. For example, big tech and universities have built AI “co-scientists” that read research papers and even design lab experiments ^{[1] [1]}. Drug companies use AI to model how new medicines move through the body and to predict toxic effects, cutting development time by years ^{[1] [1]}.

Some labs now use “robot-labs” where AI and machines run experiments automatically ^{[2] [1]}. These tools can speed up finding interesting compounds or vaccine ideas, but human scientists still check everything carefully.

Researchers and companies are excited about AI’s promise but cautious about costs and risks. New AI tools for medical research are available (for example, start-ups like Insitro work with big pharma to find drug leads ^[6]), and studies suggest AI could cut drug discovery costs and time by about half ^{[1] [1]}. However, setting up AI systems is expensive: labs need powerful computers, lots of data, and skilled staff, so only well-funded teams can afford it now ^{[5] [6]}.

Medical science is also tightly regulated. Specialists point out that AI should be used with care – extensive testing and human review are needed to keep patients safe and avoid mistakes ^{[4] [5]}. In medicine, people trust human judgment for life-and-death decisions, so AI is seen as a partner, not a replacement.

Overall, AI is growing as a helpful tool, but it will take time and human oversight for it to be widely adopted in medical research ^{[1] [5]}. Human creativity, critical thinking, and ethics remain central to this work, even as AI makes some tasks faster and easier ^{[5] [5]}.