LAS VEGAS — The Food and Drug Administration announced Tuesday that it will ease regulation of digital health products, following through on the Trump administration’s promises to deregulate artificial intelligence and promote its widespread use.

FDA Commissioner Marty Makary indicated that one of the agency’s priorities is fostering an environment that’s good for investors, and that FDA regulation needs to move “at Silicon Valley speed.” He announced the changes during an address to conference attendees at the Consumer Electronics Show.

The agency will soften its approach to the regulation of clinical decision support software, which include AI-enabled products that help doctors navigate diagnoses and treatment options. The agency previously considered products that delivered a single recommendation as FDA-regulated medical devices. Now, those products can enter the market without FDA review as long as they fulfill the agency’s other criteria for escaping regulation. 

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One day, probably in the next year or two, a company will claim it has put the first artificial-intelligence-designed antibody in the clinic. But the industry is divided on what “AI-designed” really means, and how close we are to the technology truly being able to design a medicine.

What exactly does it mean for an antibody to be designed by AI? There are two schools of thought among the antibody and protein AI researchers STAT interviewed. For some, if a computer designs the basic antibody sequence that scientists later tweak to make a clinical candidate, that counts as “AI-designed.” Others say that to be truly AI-designed, an antibody should be ready to go straight into the clinic from the computer, no further lab work needed — a much higher bar to clear. 

In 2025, researchers made it clear that AI can meet the first, easier definition of making an “AI-designed” antibody. But while some startups claim to be making antibodies that are ready to go straight into the clinic, and despite investors shoveling more money into AI-native biotechs at higher valuations compared to traditional biotech startups, even pharma and antibody experts embracing AI find it hard to believe that de novo protein design AI models can meet or beat traditional techniques.

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