by Seth Raker | Apr 12, 2022 | Commentary |
Artificial Intelligence has the potential to improve health care systems worldwide. For example, AI can optimize workflow in hospitals, provide more accurate diagnoses, and bring better medical treatments to patients. However, medical AI also creates challenges that we, as a society, need to face. This article does not attempt a comprehensive listing but focuses on three important obstacles: safety, transparency, and privacy. Regulating Safety It is of utmost importance that the use of AI is safe and effective. How do we ensure that AI trained in a particular setting is going to be reliable once deployed? As a real example, IBM Watson for Oncology uses AI algorithms to assess patients’ medical records and help physicians explore cancer treatment options. However, it has come under fire for not delivering on expectations and providing “unsafe and incorrect” recommendations. The problem appears to have been in the training, which is not based on an analysis of historical patient records, but rather only a few “synthetic cases.” This highlights the need for datasets that are reliable and valid. The software is only as good as the data its trained on, so the collection and curating of data can be the most critical part of deploying effective AI. Also, algorithms need further refinement and continuous updating. This separates AI from other medical devices, which do not have the ability to continuously learn. In terms of legal regulation, some medical AI-based products must undergo review by the FDA. A medical device is defined under section 201(h) of the Federal Food, Drug, and Cosmetic Act. While some software functions does not qualify (see Section...
