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Healthcare AI Daily

Healthcare AI Daily

Written by: Raphael T. Malikian
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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.© 2026 Raphael T. Malikian Science
Episodes
  • AI That Reasons Like a Clinician for Mental Health | Mental-R1

    AI That Reasons Like a Clinician for Mental Health | Mental-R1
    Jun 15 2026
    Mental-R1: Can we teach AI to reason like a clinician for mental health assessment? Researchers at Oxford developed CRPO (Cognitive Relative Policy Optimization), a reinforcement learning framework that aligns LLM reasoning with human cognitive processes for mental health assessment. Mental-R1 outperforms GPT-5, DeepSeek-R1, and domain-specific models across 8 mental health datasets covering depression, anxiety, suicide risk, stress, and loneliness. Key findings: - 80.8% accuracy on depression severity classification - 10.4 percentage point average improvement in weighted F1-score - Human-like reasoning pattern: broad exploration followed by confident decisions - Outperforms GPT-5, DeepSeek-R1, GPT-4o, and all domain-specific models Paper: arxiv.org/abs/2606.13176 Authors: Xin Wang, Boyan Gao, Yibo Yang, David A. Clifton #HealthcareAI #MentalHealth #ClinicalAI #MachineLearning #ReinforcementLearning #DigitalHealth #AIResearch #LargeLanguageModels #MentalHealthAI #AIinMedicine #HealthTech MEDICAL DISCLAIMER: This podcast is for educational and informational purposes only. It is not intended to diagnose, treat, cure, or prevent any medical condition, and should not be relied upon as medical advice. The content discussed reflects published research and does not constitute clinical recommendations. If you have any health concerns or medical questions, please consult a licensed healthcare professional immediately. 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. Created by Raphael T. Malikian, MBBS, BSc (Hons) For comments, questions, or concerns: rtmalikian@gmail.com YouTube: https://www.youtube.com/@RaphaelMalikian-g4h YouTube Channel: 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.
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    2 mins
  • AI Reads Bacterial Fingerprints to Predict Antibiotic Resistance | Healthcare AI Daily

    AI Reads Bacterial Fingerprints to Predict Antibiotic Resistance | Healthcare AI Daily
    Jun 13 2026
    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 on recent data - 89,026 mass spectrometry records, 274 deep learning models - 26 models for bacterial ID, 248 for antibiotic resistance prediction - Integrated chatbot for antibiotic recommendation with kidney-function dosing A resistance AUC of 0.61 is not a slam dunk - but it is decision support, not a replacement for lab culture. The open-source pipeline means any hospital can adapt it locally. Paper: https://doi.org/10.1038/s41746-026-02879-w Like and subscribe for daily Healthcare AI episodes. New videos every day on the latest in clinical AI research. #HealthcareAI #MachineLearning #AntibioticResistance #AI #DeepLearning #ClinicalAI #MALDITOF #DigitalMedicine #AntimicrobialResistance #InfectiousDisease Watch this episode on YouTube: https://youtu.be/FBFC-zD0Vps YouTube Channel: 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.
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    2 mins
  • When AI Makes Things Up in Medicine: What Actually Works | Healthcare AI Daily

    When AI Makes Things Up in Medicine: What Actually Works | Healthcare AI Daily
    Jun 15 2026
    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 Strategies. BMC Health Services Research (2026). DOI: 10.1186/s12913-026-14851-1 PMID: 42251377 What strategies has your team tried to reduce AI hallucinations? Share in the comments. Like and subscribe for daily Healthcare AI videos. #HealthcareAI #ArtificialIntelligence #LLM #PatientSafety #AIMedical #RAG #HumanInTheLoop #AISafety #ClinicalAI #HealthInformatics Watch this episode on YouTube: https://youtu.be/nVDuULt65XQ YouTube Channel: 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.
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    2 mins
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