FDA has raised the bar on real-world evidence (RWE) studies with its new draft guidance. FDA’s expectations that RWE studies be good quality, rigorous, and conducted under a protocol and with IRB approval are not new. But the depth of data assessment, documentation, and requirements for submission outlined in this guidance have been augmented beyond what may be reasonable. A greater desire by regulators for clinical data to support medical device safety and effectiveness is here to stay, not just at FDA, but globally. While the motivations are genuinely for the benefit of public health, the reality is that clinical and observational research is expensive, complicated, time consuming, and burdensome. These costs will ultimately be passed down to payers and patients. I am not sure the incremental confidence FDA has in its regulatory decisions is worth the cost.
Read MoreA recent article published in NPJ Digital Medicine found that large language models (LLM) used in healthcare propagate racist medical advice that has largely been debunked. This is not a surprise to me, but it is an alarming finding that reminds us that the data we use to train our AI-driven devices and healthcare decision programs is horribly biased. Industry and regulators must ensure that data being used to train these devices are diverse, equitable, and representative to prevent potential patient harm.
Read MoreAt the October NORD conference, Jeff Shuren suggested that AI-driven medical devices could be designed and evaluated using an FDA AI Assurance Lab. He suggested that this lab could house quality data on which AI algorithms could be trained. But, is this idea too good to be true? Where will the data come from? What therapeutic areas will it include? And will industry play nice in FDA’s AI Assurance Lab sandbox? Given the need for rational, consistent and transparent AI regulatory policy, this could be a great idea. But, the practical application of such a plan is unclear.
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