Joseph O'Brien

In-silico clinical trials (ISCTs) for medical devices and products are computer-based simulations of material clinical trials, often of randomized clinical trials (RCTs) on populations of ‘virtual’ patients. A major aim for developing ISCTs is to use them to complement (or potentially sometimes replace) material trials for providing evidence about the performance of medical products. While these methods are beginning to see use, especially as additional sources of evidence that regulatory agencies allow, they remain relatively under-theorized by philosophers of science. In this paper, we aim to introduce philosophers of science to ISCTs, focusing on ISCTs that generate results using knowledge-based models. Although we identify a number of areas in philosophy of science that we expect can make significant contributions in thinking about ISCTs, from work on measurement and models to that on computer simulations, our own offerings are on issues related to the use of RCTs themselves, like external and internal validity, and on the various regulatory standards for their use. Because of the practical importance of their use as evidence for effectiveness and safety by regulatory agencies, we focus most on how in-silico randomized control trials (ISRCTs) compare with material RCTs done on real patients, which most regulatory agencies see as the gold standard for evidence of intervention effectiveness. Our primary examples are of medical devices since this seems to be the area where suggestions for ISCT use is most developed. Our hope is that this introductory paper will prompt other philosophers of science to delve deeper into this extremely interesting and important area of applied science that can have real practical effects on our lives as ISCT results are increasingly allowed as evidence for medical effectiveness and safety.

AI integration into scientific communities promises accelerated discovery but raises concerns about detrimental homogenization. We develop an NK landscape model to explore these promises and risks. We find that non-personalized AI systems that offer uniform guidance yield benefits only under a narrow conjunction of specific problem structure, practices, and baseline research capabilities, becoming harmful otherwise. We implement two proposed mitigations: randomization and personalization. While randomization's utility remains restricted to decomposable problems, personalization preserves or enhances diversity, enabling benefits across conditions. Overall, our results highlight the importance of shifting perspectives in systemic AI evaluations from tool adoption to institutional adaptation.