Healthcare AI Daily

Your daily dose of clinical AI research. Each episode breaks down the latest peer-reviewed research at the intersection of artificial intelligence and healthcare — from large language models in clinical decision support to computer vision in pathology, from federated learning for patient privacy to AI-powered diagnostics. Hosted by Raphael T. Malikian, MBBS, BSc (Hons), Healthcare AI Daily bridges the gap between cutting-edge AI research and real-world clinical impact. Whether you're a clinician, researcher, technologist, or simply curious about how AI is transforming medicine, each episode delivers clear, evidence-based insights in under 3 minutes. Watch every episode on YouTube: https://www.youtube.com/@RaphaelMalikian-g4h Created by Raphael T. Malikian (rtmalikian@gmail.com). In true AI fashion, this podcast was created with AI tools including text-to-speech using Microsoft Edge TTS and Hermes Agent by Nous Research. All research content is based on peer-reviewed publications. For comments, questions, or concerns, email: rtmalikian@gmail.com New episodes every day. Subscribe wherever you listen to podcasts.

Healthcare AI Daily is a general podcast hosted by Unknown Host, with 18 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts Healthcare AI Daily, a general show with 18 episodes published.

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• Healthcare AI Daily

  Healthcare AI Daily: Reasoning LLMs Can Still Stereotype Disease Cases

  Jun 18, 2026—S0

  Raphael T. Malikian, MBBS, BSc (Hons) translates healthcare AI research into practical, clinically grounded questions for builders, clinicians, researchers, and governance teams. GitHub: https://github.com/rtmalikian LinkedIn: http://www.linkedin.com/in/raphael-t-malikian-mbbs-bsc-hons-71075436a --- Healthcare AI Daily translates one recent peer-reviewed healthcare AI article into a short practical briefing. Today: why newer reasoning large language models can still generate stereotyped medical vignettes — and why healthcare teams should monitor representational bias before using synthetic cases in education, evaluation, or workflow design. Source article Title: Evaluating the Potential of Reasoning Large Language Models to Perpetuate Racial and Gender Disease Stereotypes in...

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• Healthcare AI Daily

  AI Reads Bacterial Fingerprints to Predict Antibiotic Resistance | Healthcare AI Daily

  Jun 18, 2026—S0

  Right now, doctors wait one to three days to learn which antibiotics will work for a serious infection. A new deep learning system called ANTIBIOTIC reads mass spectrometry data hospitals already collect and predicts resistance in minutes. Antimicrobial resistance kills over a million people a year. The core problem is speed - conventional testing takes one to three days. During that window, doctors prescribe broad-spectrum antibiotics, driving more resistance. Published in NPJ Digital Medicine by Wang, Tsao, Hsieh et al. from National Taiwan University Hospital. Key findings: - Bacterial identification AUC: 0.99 (internal), 0.96 (external) - Resistance prediction AUC: 0.94 (internal), 0.61 after fine-tuning...

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• Healthcare AI Daily

  When AI Makes Things Up in Medicine: What Actually Works | Healthcare AI Daily

  Jun 18, 2026—S0

  What happens when healthcare AI hallucinates? A new systematic review of 44 studies identifies 7 strategies that actually reduce AI errors in medicine. Sanjay Basu and Benjamin Huynh from UC San Francisco and Johns Hopkins reviewed every major approach - from retrieval-augmented generation to human-in-the-loop oversight - and found that combining strategies consistently beats any single method. Key findings: - RAG reduced hallucinations by 30-50% - Human-in-the-loop achieved up to 95% reduction - Combined approaches outperformed all single methods - Proposed MediHall severity scale for prioritizing AI errors Paper: Basu S, Huynh B. Mitigating Hallucinations in Healthcare AI: A Systematic Review of Evidence-Based...

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• Healthcare AI Daily

  Your Home Could Detect a Stroke Before It Happens | Healthcare AI Daily

  Jun 18, 2026—S0

  Can contactless sensors in your home detect cerebrovascular disease before a stroke happens? A new study from South Korea placed IoT sensors in the homes of 1,224 older adults and trained an AI to watch for subtle changes in movement, sleep, and activity patterns. The AI identified people in the pre-stroke phase with 85% precision, and predicted who was within four weeks of diagnosis with 95% sensitivity. Evening activity patterns were the strongest signal. Published in NPJ Digital Medicine (2026). DOI: 10.1038/s41746-026-02836-7 Authors: Baek J, Cho K-H, Lim L, Chong JW Key findings: - 1,224 adults aged 65+, 13,362 two-week observation windows - AUPRC 0.85...

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• Healthcare AI Daily

  Healthcare AI Daily: GPT-5, Grok 4, DeepSeek R1 on Blood Count Reports

  Jun 18, 2026—S0

  Raphael T. Malikian, MBBS, BSc (Hons) translates healthcare AI research into practical, clinically grounded questions for builders, clinicians, researchers, and governance teams. GitHub: https://github.com/rtmalikian LinkedIn: http://www.linkedin.com/in/raphael-t-malikian-mbbs-bsc-hons-71075436a --- Healthcare AI Daily translates one healthcare AI paper into a short practical briefing for builders, clinicians, researchers, and governance teams. Today: how three frontier AI models performed when interpreting real blood count reports from patients with blood diseases, and where each one stumbled. Source article Title: Performance Evaluation of GPT-5, Grok 4, and DeepSeek R1 in Interpreting Complete Blood Count Reports for Hematologic Diseases: Retrospective...

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