I work for a large pharma, and the primary uses for AI that I've seen here are 1) biomarker development and 2) a more precise or accurate, and reproducible way

I work for a large pharma, and the primary uses for AI that I've seen here are 1) biomarker development and 2) a more precise or accurate, and reproducible way to measure medical signals. Biomarkers predict outcome, of drug effect or of toxins/safety. When used preclinically (non human models) biomarkers continue to be highly valued internally. If a computational model can reliably anticipate outcome, it can shorten trial time and cost. However, in clinical/human use, biomarkers seem increasingly fraught in recent years. However the FDA seems to be less receptive to surrogate outcome predictors, and more demanding of concrete quantifiable clinical adverse events (e.g. the LDL/HDL ratio vs stroke, BMD vs bone breakage, A1C vs retinopathy). As such, I'd be circumspect about developing biomarkers that lead directly to diagnosis. Like the PSA test, even a strong biomarker that also introduces false positives or uncertainty is likely to face opposition to adoption in standard medical practice. As to the use of AI (esp. pattern recognition) to better measure drug response or toxin effect, this seems to be well received, at least for in-house use. Automation of signal acquisition or analysis, if it can reliably improve on the status quo, in my experience, is well received by my employer. As a drug development cost cutting measure or as a more reliable rater of symptom measurement, AI seems to be win-win. That doesn't mean I see a wholesale rush to adopt AI-related tech here, but the interest seems to be steady and positive. Presumably this should lead to greater use of AI by manufacturers of medical instruments, which frankly I have not seen (though Siemens certainly has hired its share of quants, presumably to serve such ends. However, these folks may well spend most of their time working their magic on external contracts.) Often in pharma, I think AI, like math models, are perceived by biologists to be too synthetic and abstract, and lack the credibility of a well trod mouse model. Unless AI/quant models lead to a < .05 T Test and a visible separation of error bars between groups, it's unconvincing statistically. And unless the AI can be tied _convincingly_ and directly to an underlying chemical mechanism, it's unconvincing biologically. Scientists are a tough audience.