James Fleming emphasized that leveraging Gen AI for scientific advancements is complex, stating, "Doing scientific AI, as opposed to creating publicly available large language models, is quite a different discipline. You're operating in a tightly bounded scientific world where you've got to prove your hypothesis." He highlights the crucial need for precision, noting that conclusions from AI must be incredibly accurate to be viable in real-world applications.
Fleming further elaborated on the necessity of provability in scientific AI applications, stating, "Provability is critical, particularly if you're thinking about something with a pathway to real-world impact in a clinic or as a medical device." This underscores the importance of having rigorous evidence when presenting AI-driven innovations in the medical field.
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