In this episode of The Med AI Capsule, we look at how medicine is racing ahead and why explainable AI has become the central requirement for safe adoption. We unpack how tools like heat maps, SHAP scores, and rule extraction pull back the curtain on complex models and why these explanations are only clues about the model’s logic, not biological truth. The goal is simple: build systems clinicians can trust without surrendering judgment.
We then move through the latest research and deployments. Speech-based models for early dementia show strong accuracy, but clinical readiness remains low because workflows are ignored. On the ground, startups like Vuha Med Data are scaling cervical cancer screening in low-resource environments, while AI tools are already improving safety, communication, and clinician wellbeing through infection prediction, patient-facing chat systems, and ambient AI scribes that cut burnout within weeks.
We close with the policy and professional shifts that this rapid growth demands. New AMA requirements and India’s DPDP Act are reshaping training and data governance. And specialties like anesthesiology face an uncomfortable but necessary question: if machines handle the mechanics, how should clinicians redefine their role? The episode ends on the core challenge that will shape the decade ahead: using AI’s speed without losing the human connection that defines care.
Note: This audio podcast is an extension of The Med AI Capsule newsletter by Dr Avneesh Khare. You can explore more at www.avneeshkhare.com.
Disclaimer: This audio podcast was generated using Google NotebookLM and is based on content from the related newsletter issue. While all the information is curated from reliable sources, it is only intended for educational and informational purposes. AI LLMs can make mistakes. Please check all the important information. For any issues or inaccuracies, please reach out at [email protected].
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By Updates on Artificial Intelligence & Emerging Technologies in Medicine 🤖In this episode of The Med AI Capsule, we look at how medicine is racing ahead and why explainable AI has become the central requirement for safe adoption. We unpack how tools like heat maps, SHAP scores, and rule extraction pull back the curtain on complex models and why these explanations are only clues about the model’s logic, not biological truth. The goal is simple: build systems clinicians can trust without surrendering judgment.
We then move through the latest research and deployments. Speech-based models for early dementia show strong accuracy, but clinical readiness remains low because workflows are ignored. On the ground, startups like Vuha Med Data are scaling cervical cancer screening in low-resource environments, while AI tools are already improving safety, communication, and clinician wellbeing through infection prediction, patient-facing chat systems, and ambient AI scribes that cut burnout within weeks.
We close with the policy and professional shifts that this rapid growth demands. New AMA requirements and India’s DPDP Act are reshaping training and data governance. And specialties like anesthesiology face an uncomfortable but necessary question: if machines handle the mechanics, how should clinicians redefine their role? The episode ends on the core challenge that will shape the decade ahead: using AI’s speed without losing the human connection that defines care.
Note: This audio podcast is an extension of The Med AI Capsule newsletter by Dr Avneesh Khare. You can explore more at www.avneeshkhare.com.
Disclaimer: This audio podcast was generated using Google NotebookLM and is based on content from the related newsletter issue. While all the information is curated from reliable sources, it is only intended for educational and informational purposes. AI LLMs can make mistakes. Please check all the important information. For any issues or inaccuracies, please reach out at [email protected].