The Spectrum of AI in Healthcare: Understanding the Levels of Intelligence in AI
AI in healthcare can significantly enhance diagnosis speed, personalization of treatments, and automate drug discovery. Healthcare companies can benefit from AI by understanding its capabilities and considering the quality and diversity of data it uses. The discussion emphasizes the importance of balancing accuracy with tolerable errors in AI implementations.
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Key takeaways
AI in healthcare improves disease diagnostic detection speed and treatment personalization.
A balanced approach to AI in healthcare factors in false negatives and false positives effectively.
Evaluating the quality and diversity of data used in AI models is crucial.
Artificial intelligence (AI) is not just for gamers or factory warehouse robots—it can, quite literally, save lives in the healthcare sector. AI in healthcare can be used to improve diagnostic detection speed for diseases, improve personalization of medical treatments, and automate drug discoveries.
How should healthcare companies be approaching AI use, and what benefits and drawbacks can they expect to see when deploying AI?
On today’s episode of Iodine Intelligence, Empowering Intelligent Care, host Lauren Hickey, Content Strategist at Iodine Healthcare, is joined by Priti Shah, Chief Product & Technology Officer at Iodine Software, to discuss applications for AI in healthcare and how healthcare companies can approach use cases for AI models.
Hickey and Shah also discussed…
- Applications of AI in automation, improving efficiency of judgement, timeliness, and consistency of results
- A framework for guiding questions in approaching AI use
- Real-world examples of how a company might answer questions about their AI
Shah knows AI in healthcare is not the end-all, be-all. “We have to understand that no model is perfect, and you have to choose one balance of false negatives and false positives you can live with.” She suggested, “One of the biggest things people should hone in on is how much data was this AI model trained on, and what is the quality and diversity of that data?”
Priti Shah is Chief Product & Technology Officer at Iodine Software. She is an experienced General Management Executive with more than two decades of experience in revenue growth and market expansion. Before her current role, Shah was Chief Product Officer of Finvi, VP, Product & Solutions at Wolters Kluwer Health, and VP, Product Strategy & Corporate Development, amongst other positions. Shah attended Harvard Business School’s General Management Program and the Rochester Institute of Technology, where she earned an MBA in Marketing & Strategic Management.
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