This week’s AI in Healthcare update covers four key developments: an NEJM AI randomized trial finding AI literacy training did not prevent automation bias from intentionally erroneous LLM diagnostic suggestions; FDA clearance and a CMS reimbursement pathway for Bunkerhill’s AI tools quantifying coronary and aortic valve calcium on routine contrast, non-gated chest CT (with limited independent peer-reviewed validation noted); a 48-trial Bayesian network meta-analysis (34,106 participants) reporting five AI colonoscopy systems improved adenoma detection rate but not advanced adenomas or sessile serrated lesions; and a Nature Medicine perspective urging higher evidentiary standards and patient-centered outcomes before claiming AI improves healthcare. 00:00 Weekly AI Healthcare Briefing00:48 AI Literacy vs Automation Bias02:22 FDA Cleared Calcium Detection03:55 AI Colonoscopy Meta Analysis05:54 Raising the Evidence Bar07:23 Wrap Up and Next Week

This week’s AI in Healthcare update covers four key developments: an NEJM AI randomized trial finding AI literacy training did not prevent automation bias from intentionally erroneous LLM diagnostic suggestions; FDA clearance and a CMS reimbursement pathway for Bunkerhill’s AI tools quantifying coronary and aortic valve calcium on routine contrast, non-gated chest CT (with limited independent peer-reviewed validation noted); a 48-trial Bayesian network meta-analysis (34,106 participants) reporting five AI colonoscopy systems improved adenoma detection rate but not advanced adenomas or sessile serrated lesions; and a Nature Medicine perspective urging higher evidentiary standards and patient-centered outcomes before claiming AI improves healthcare.00:00 Weekly AI Healthcare Briefing00:48 AI Literacy vs Automation Bias02:22 FDA Cleared Calcium Detection03:55 AI Colonoscopy Meta Analysis05:54 Raising the Evidence Bar07:23 Wrap Up and Next Week

AI in Healthcare

AI in Healthcare Weekly (Apr 27-May 4, 2026) Automation Bias, FDA CMS Cleared Calcium CT AI, Colonoscopy AI Meta Analysis, Evidence Standards

In this week's episode of AI in Healthcare, your concise update for healthcare professionals on artificial intelligence in clinical medicine, we examine four developments from the week of April 20–27, 2026.A New England Journal of Medicine Perspective on Utah's AI-assisted prescription-renewal sandbox pilot — a state-regulated program with pharmacist-mediated escalation, distinct from the FDA pathway for software as a medical device — and the corresponding American Hospital Association governance panel on April 20.A multicenter Korean validation study in JMIR Medical Informatics introducing patient-wise recalibration to mitigate model drift in AI electrocardiography for left ventricular systolic dysfunction (reported AUC 0.956 internal, 0.940 external on follow-up pairs).A randomized controlled trial in JMIR Mental Health in which both a structured generative AI therapy chatbot and plain GPT-4o produced significant PHQ-9 reductions versus control, with no significant difference between active arms (n = 147).A methodological comparison in JMIR in which XGBoost (micro-F1 0.815) outperformed a LoRA-fine-tuned LLaMA-3 (0.780) on ASA Physical Status classification.Evidence-based, reference-linked, ~5 minutes. For healthcare professionals only. 00:00 Weekly Headlines00:31 Utah Prescribing Sandbox02:09 Governance Takeaways02:38 Drift Mitigation Study04:06 GenAI Depression Trial05:35 LLM vs XGBoost Methods06:48 Wrap Up and References REFERENCES<ol><li>Utah Prescription-Renewal Pilot. NEJM Perspective, April 2026. DOI: <a href="https://doi.org/10.1056/NEJMp2601148" rel="noopener noreferrer" target="_blank" style="color: rgb(0, 115, 230);">10.1056/NEJMp2601148</a></li><li>Utah Department of Commerce / Doctronic announcement, January 2026: <a href="https://commerce.utah.gov/2026/01/06/news-release-utah-and-doctronic-announce-groundbreaking-partnership-for-ai-prescription-medication-renewals/" rel="noopener noreferrer" target="_blank" style="color: rgb(0, 115, 230);">commerce.utah.gov</a></li><li>AHA Panel — AI in Health Care: Navigating Policy, Regulation, and the Road Ahead. April 20, 2026: <a href="https://www.aha.org/news/headline/2026-04-20-ai-health-care-experts-discuss-future-ai-practices-and-policies" rel="noopener noreferrer" target="_blank" style="color: rgb(0, 115, 230);">aha.org</a></li><li>Lee S, Son J-W, Kim S-A, et al. Deep Learning Model Using Transfer Learning for Detecting Left Ventricular Systolic Dysfunction. JMIR Med Inform. April 24, 2026. DOI: <a href="https://doi.org/10.2196/83127" rel="noopener noreferrer" target="_blank" style="color: rgb(0, 115, 230);">10.2196/83127</a></li><li>Kuta B, Novak L, Zidkova R, et al. Effectiveness of a Fully Automated Mobile Therapeutic Versus a General Chatbot in Reducing Depression and Anxiety. JMIR Ment Health. April 22, 2026. DOI: <a href="https://doi.org/10.2196/82642" rel="noopener noreferrer" target="_blank" style="color: rgb(0, 115, 230);">10.2196/82642</a></li><li>Chen M-C, Ruan S-J, Wu J-H, Chen P-F. Classifying ASA Physical Status With a Low-Rank-Adapted Large Language Model. J Med Internet Res. April 21, 2026. DOI: <a href="https://doi.org/10.2196/89540" rel="noopener noreferrer" target="_blank" style="color: rgb(0, 115, 230);">10.2196/89540</a></li></ol>  Disclaimer: For healthcare professionals only. Not medical advice. Opinions expressed do not represent any institution.#AIinHealthcare #ClinicalAI #DigitalHealth #FDA #AIRegulation #AIECG #GenerativeAI #LLM #NEJM #JMIR

In this week's episode of AI in Healthcare, your concise update for healthcare professionals on artificial intelligence in clinical medicine, we examine four developments from the week of April 20–27, 2026.A New England Journal of Medicine Perspective on Utah's AI-assisted prescription-renewal sandbox pilot — a state-regulated program with pharmacist-mediated escalation, distinct from the FDA pathway for software as a medical device — and the corresponding American Hospital Association governance panel on April 20.A multicenter Korean validation study in JMIR Medical Informatics introducing patient-wise recalibration to mitigate model drift in AI electrocardiography for left ventricular systolic dysfunction (reported AUC 0.956 internal, 0.940 external on follow-up pairs).A randomized controlled trial in JMIR Mental Health in which both a structured generative AI therapy chatbot and plain GPT-4o produced significant PHQ-9 reductions versus control, with no significant difference between active arms (n = 147).A methodological comparison in JMIR in which XGBoost (micro-F1 0.815) outperformed a LoRA-fine-tuned LLaMA-3 (0.780) on ASA Physical Status classification.Evidence-based, reference-linked, ~5 minutes. For healthcare professionals only.00:00 Weekly Headlines00:31 Utah Prescribing Sandbox02:09 Governance Takeaways02:38 Drift Mitigation Study04:06 GenAI Depression Trial05:35 LLM vs XGBoost Methods06:48 Wrap Up and ReferencesREFERENCESUtah Prescription-Renewal Pilot. NEJM Perspective, April 2026. DOI: 10.1056/NEJMp2601148Utah Department of Commerce / Doctronic announcement, January 2026: commerce.utah.govAHA Panel — AI in Health Care: Navigating Policy, Regulation, and the Road Ahead. April 20, 2026: aha.orgLee S, Son J-W, Kim S-A, et al. Deep Learning Model Using Transfer Learning for Detecting Left Ventricular Systolic Dysfunction. JMIR Med Inform. April 24, 2026. DOI: 10.2196/83127Kuta B, Novak L, Zidkova R, et al. Effectiveness of a Fully Automated Mobile Therapeutic Versus a General Chatbot in Reducing Depression and Anxiety. JMIR Ment Health. April 22, 2026. DOI: 10.2196/82642Chen M-C, Ruan S-J, Wu J-H, Chen P-F. Classifying ASA Physical Status With a Low-Rank-Adapted Large Language Model. J Med Internet Res. April 21, 2026. DOI: 10.2196/89540 Disclaimer: For healthcare professionals only. Not medical advice. Opinions expressed do not represent any institution.#AIinHealthcare #ClinicalAI #DigitalHealth #FDA #AIRegulation #AIECG #GenerativeAI #LLM #NEJM #JMIR

AI in Healthcare -- April 27, 2026 Sandbox Regulation, Drift Mitigation, and LLM Limits

In this episode of the AI in Healthcare podcast, we explore new research on using gradient-boosted models to predict coronary artery disease. 00:00 Introduction to AI in Healthcare Podcast00:09 Gradient-Boosted Models in Coronary Artery Disease Prediction00:47 Improving Referral Pathways with Transparent Models01:18 Pragmatic Steps for Clinical Implementation01:40 Key Takeaways and Recommendations01:55 Conclusion Williams MC, Guimaraes ARM, Jiang M, Kwieciński J, Weir-McCall JR, Adamson PD, et al. Machine learning to predict high-risk coronary artery disease on CT in the SCOT-HEART trial. Open Heart. 2025 Sep 1;12(2):e003162. doi:10.1136/openhrt-2025-003162. PMCID: PMC12406813. PMID: 40889953.

In this episode of the AI in Healthcare podcast, we explore new research on using gradient-boosted models to predict coronary artery disease. 00:00 Introduction to AI in Healthcare Podcast00:09 Gradient-Boosted Models in Coronary Artery Disease Prediction00:47 Improving Referral Pathways with Transparent Models01:18 Pragmatic Steps for Clinical Implementation01:40 Key Takeaways and Recommendations01:55 ConclusionWilliams MC, Guimaraes ARM, Jiang M, Kwieciński J, Weir-McCall JR, Adamson PD, et al. Machine learning to predict high-risk coronary artery disease on CT in the SCOT-HEART trial. Open Heart. 2025 Sep 1;12(2):e003162. doi:10.1136/openhrt-2025-003162. PMCID: PMC12406813. PMID: 40889953.  

Sharpening Coronary CT Triage: Practical AI Tools for Chest Pain Clinics

In this episode of the AI in Healthcare podcast, we break down a new study by Jeong and colleagues that explores the impact of AI assistance on radiologist reading times and workflow efficiency. One‑line takeaway: AI assistance reduced reading time and improved throughput for bone‑age radiograph interpretation in a real‑world retrospective cohort. ReferenceThe Impact of Artificial Intelligence on Radiologists’ Reading Time in Bone Age Radiograph Assessment Citation: Jeong S, Han K, Kang Y, et al. Journal of Imaging Informatics in Medicine. 2025 Aug;38(4):1915‑1923.https://pubmed.ncbi.nlm.nih.gov/39528879/

In this episode of the AI in Healthcare podcast, we break down a new study by Jeong and colleagues that explores the impact of AI assistance on radiologist reading times and workflow efficiency.One‑line takeaway: AI assistance reduced reading time and improved throughput for bone‑age radiograph interpretation in a real‑world retrospective cohort.ReferenceThe Impact of Artificial Intelligence on Radiologists’ Reading Time in Bone Age Radiograph Assessment Citation: Jeong S, Han K, Kang Y, et al. Journal of Imaging Informatics in Medicine. 2025 Aug;38(4):1915‑1923.https://pubmed.ncbi.nlm.nih.gov/39528879/

AI assisted Pediatric Bone Age Workflow Study

Join us on the AI in Healthcare podcast as we discuss into the groundbreaking development of VentAI, a reinforcement learning algorithm designed to recommend optimal ventilator settings for ICU patients. 00:00 Introduction to AI in Healthcare00:06 VentAI: Revolutionizing Ventilator Settings00:20 Study and Validation of VentAI00:54 Performance and Comparison with Clinician Care01:37 The Role of AI in Clinical Decision Making01:56 Future of AI in Intensive Care02:02 Conclusion and Follow Us

Join us on the AI in Healthcare podcast as we discuss into the groundbreaking development of VentAI, a reinforcement learning algorithm designed to recommend optimal ventilator settings for ICU patients. 00:00 Introduction to AI in Healthcare00:06 VentAI: Revolutionizing Ventilator Settings00:20 Study and Validation of VentAI00:54 Performance and Comparison with Clinician Care01:37 The Role of AI in Clinical Decision Making01:56 Future of AI in Intensive Care02:02 Conclusion and Follow Us

Revolutionizing ICU Care How AI Optimizes Ventilator Settings

Updates into the world of artificial intelligence and explore the most recent trends and developments in healthcare.

Updates into the world of artificial intelligence and explore the most recent trends and developments in healthcare. 

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AI in Healthcare

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