AI Explainability: What that Means and Why it Matters in the Medical Device Industry
This is a podcast episode titled, AI Explainability: What that Means and Why it Matters in the Medical Device Industry. The summary for this episode is: What is AI explainability? Why does it matter in health care? On today’s episode, we have Marla Phillips, director of Xavier Health, who shares why AI will transform how the medical device industry operates. Xavier Health brings the FDA and pharmaceutical and medical device industries together in a collaborative setting to break down barriers and improve patients’ health. Overcome the media-generated hype and fear of AI to discover its benefits when using it responsibly. We can do better with it, than without it. SOME OF THE HIGHLIGHTS OF THE SHOW INCLUDE: ● AI Explainability: Part of transparency of how end user can have confidence in the outcome of AI. Explainability links credibility of input to the output. ● AI has been around since the 1950s, but its use is new to some people. The AI Summit shows how it works. ● Pivoting from being reactive to proactive: Advancing use of AI to identify correlations between data for improvement of the quality of products/patients. ● Some devices have digital health components. There’s movements around real-world data for information to go to manufacturers to evaluate performance. ● Continuous Product Quality Assurance team encourages review and assessment of all the data. AI can be used to identify conditions that lead to failure. ● Where is the data? GMP, non-GMP, financial, weather, and other kinds of data that impacts product quality. Use AI to take out garbage, find what’s meaningful. ● AI is a continuously learning system. How to evaluate it? How did it reach its outcome? How to demonstrate credibility? How to train algorhythm? ● Challenges of implementing AI include figuring out how to demonstrate credibility of AI output when not using validation and not having access to electronic data.