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

    Your Home Could Detect a Stroke Before It Happens | Healthcare AI Daily
    Jun 13 2026
    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 for prodromal (pre-diagnosis) identification - AUROC 0.91 for classifying diagnosed patients - 95% sensitivity, 97% specificity for imminent diagnostic risk - Bedtime hours (10 PM - 2 AM) and evening hours (6-10 PM) most informative - Even indoor humidity correlated with cerebrovascular risk This is retrospective research. Prospective validation is the critical next step. What do you think about AI monitoring in the home? Drop your thoughts in the comments. Like and subscribe for daily Healthcare AI episodes. New videos every day on the latest in clinical AI research. #HealthcareAI #Stroke #CerebrovascularDisease #SmartHome #AI #DigitalHealth #MachineLearning #ElderlyCare #IoT #NPJDigitalMedicine #StrokePrevention #RemotePatientMonitoring #AgingInPlace Watch this episode on YouTube: https://youtu.be/uv32UDNGMxs 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
  • Healthcare AI Daily: GPT-5, Grok 4, DeepSeek R1 on Blood Count Reports

    Healthcare AI Daily: GPT-5, Grok 4, DeepSeek R1 on Blood Count Reports
    Jun 13 2026
    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 Comparative Study Authors: Xianfei Ye, Xinglun Qi, Lina Fan, Qian Yu, Suming Zhou, Chunyun Ren, Dagan Yang Journal: Journal of Medical Internet Research (JMIR) Published: 5 Jun 2026 DOI: https://doi.org/10.2196/87802 Article: https://www.jmir.org/2026/1/e87802 Keywords: healthcare AI, medical AI, large language models, GPT-5, Grok 4, DeepSeek R1, blood count, CBC, hematology, clinical validation, AI hallucinations, lab medicine, AI safety, clinical deployment. This video is educational commentary, not medical advice. Source screenshots and figures are used for attributed research discussion. Like, subscribe, and enable notifications for daily Healthcare AI episodes. Healthcare AI Weekly releases every Friday at 9 AM. Watch this episode on YouTube: https://youtu.be/kauhiLUZJrA 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 Mobile App Catches Skin Cancers in Rural Brazil | Healthcare AI Daily

    AI Mobile App Catches Skin Cancers in Rural Brazil | Healthcare AI Daily
    Jun 13 2026
    In rural Brazil, there’s one dermatologist for every 17,000 people. A new AI app that works completely offline just helped health workers catch skin cancers far more accurately. Published in npj Digital Medicine (Nature), Pacheco et al. validated the tool with 131 healthcare professionals across 9 cities. Sensitivity jumped from 64% to 80% with AI assistance. In rural areas, unnecessary specialist referrals dropped 30% while cancer surgeries stayed the same. Key findings: • AI model: fine-tuned MobileNet-V3, trained on 13,569 images • 5 priority levels for malignancy risk • Offline-capable mobile app for rural deployment • Sensitivity: 0.648 → 0.804 with AI assistance • Triage effectiveness: 58.6% → 75.7% Limitations: single Brazilian state, not all skin tones represented, triage tool not diagnosis. Paper: Pacheco et al., "Towards a clinically integrated artificial intelligence tool for triage of skin cancer," npj Digital Medicine, 2026. DOI: 10.1038/s41746-026-02851-8 If this kind of breakdown is useful to you, take a second to like and subscribe — it really does help. #HealthcareAI #SkinCancer #AI #DigitalHealth #MachineLearning #ClinicalValidation #MobileHealth Watch this episode on YouTube: https://youtu.be/fdC5XCkZPjE 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
  • Can AI Read Your ECG? Testing Vision-Language Models on Real ECGs | Healthcare AI Daily

    Can AI Read Your ECG? Testing Vision-Language Models on Real ECGs | Healthcare AI Daily
    Jun 13 2026
    Can ChatGPT, Gemini, or Claude actually read an electrocardiogram? A new peer-reviewed study tested six major vision-language models on 70 real clinical ECG images. The results are a wake-up call for anyone assuming AI can replace ECG interpretation. Key findings: • Best generalist model (ChatGPT-5): 62% balanced accuracy • Atrial fibrillation sensitivity: 11% or less — missed nearly every case • ST-segment deviation sensitivity: below 25% • Specialized ECG model PULSE-7B reached 89% for rhythm classification Paper: "Performance of Vision-Enabled Large Language Models in Image-Based Electrocardiogram Interpretation: Exploratory Evaluation" Authors: Soubh, Rasenack, Haarmann, Wiedmann, Zabel, Schmidt, Suliman, Bergau Journal: Journal of Medical Internet Research, June 3, 2026 DOI: 10.2196/86692 #HealthcareAI #ECG #AIinMedicine #Cardiology #VisionLanguageModels #ClinicalAI If this kind of breakdown is useful to you, take a second to like and subscribe — it really does help. Watch this episode on YouTube: https://youtu.be/yWO7ouwb-sc 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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    3 mins
  • AI-Powered HER2 Scoring in Breast Cancer Pathology | Healthcare AI Daily

    AI-Powered HER2 Scoring in Breast Cancer Pathology | Healthcare AI Daily
    Jun 8 2026
    New AI approaches to HER2 scoring in breast cancer pathology slides, improving consistency and speed of this critical biomarker assessment. Watch this episode on YouTube: https://youtu.be/ydhDqkzKOgY 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