Pain Matters Podcast Network · AAPM - American Academy of Pain Medicine

The Future of AI in Medicine: Current State, Applications, and Evolution

·1 hr 7 min·4 clips

The episode begins by positioning AI in healthcare as a practical force with four distinct jobs. One is better outcomes for patients. Another is faster access and a smoother care experience. A third is lower delivery costs and higher productivity, and the fourth is support for clinicians and other healthcare workers. The discussion keeps coming back to the strain on medical teams. It points to clinician shortages, growing complexity in clinical medicine, and burnout as reasons AI could help support existing teams. The conversation also stresses how quickly medical knowledge keeps expanding. One speaker notes that even an hour and a half a day spent reading PubMed would not be enough to stay current. That makes a strong case for AI systems that can scan and summarize large bodies of literature. The show does not let that argument stand on its own. It quickly raises the problem of quality, because not all studies are equal and not all publications deserve the same weight. That concern leads into predatory journals and low-quality science. The episode asks whether an AI model can reliably tell the difference between a paper in a major journal and one that was effectively paid into print. It also questions whether a model can separate peer-reviewed research from material scraped from the open internet, social media, or ordinary web pages. Those are not abstract worries. They sit at the center of whether AI can make recommendations clinicians should trust. The episode then widens into a copyright and training-data debate, using the New York Times lawsuit against OpenAI and Microsoft as a focal point. The discussion treats that case as more than a legal fight over media ownership. It frames the lawsuit as a test of how training data, sourcing, and reliability will shape AI systems that are being asked to influence healthcare decisions. The result is a conversation that ties together medicine, publishing, model development, and institutional accountability. The overall tone is practical and cautioning rather than promotional. The speakers keep circling back to the same core question: how do you use AI to help clinicians and patients without confusing speed with truth? That makes the episode useful as a field guide to the current state of AI in medicine and the tensions that will shape its next phase.

As heard by us

A grounded look at AI in medicine, from access and delivery to the tougher question of trust.

Artificial intelligence in medicine is treated here as a practical systems problem, not a slogan. The focus stays on better patient outcomes, faster access to clinicians, stronger delivery, and relief for healthcare workers already under strain.

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Why you'd press play

Press play if you want AI explained as a clinical tool, not a buzzword.

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