Healthcare AI Daily

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...

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 practica...

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 practica...

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 practica...

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 practica...

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 •...

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...

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...

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% sens...

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. Ke...

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, docto...

Most healthcare AI is trained on urban hospital data. High-altitude populations with unique health needs are invisible to these systems. Shi P et al. - npj Digital Medicine (2026) DOI: 10.1038/s41746-026-02701-7 This paper pioneers agentic AI systems adapted for high-altitude populations. The agents account for how altitude changes normal physiology. Like and subscribe for daily Healthcare AI rese...

Clinical notes contain the richest patient information in any hospital - but it sits in free text that no algorithm can read at scale. Zou Y et al. - npj Precision Oncology (2026) DOI: 10.1038/s41698-026-01521-y A privacy-preserving LLM extracts prognostic data from head and neck oncology notes. The model runs locally - patient data never leaves the hospital. Like and subscribe for daily Healthcar...

Most clinical AI models fail in deployment. Not because the algorithms are wrong, but because clinicians were never asked how they should work. Feng J et al. - npj Digital Medicine (2026) DOI: 10.1038/s41746-026-02838-5 This framework embeds clinician expertise directly into the AI development pipeline, producing more trustworthy and clinically meaningful predictions. Like and subscribe for daily...

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 depre...

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...

Examining how well LLMs calibrate their confidence when interpreting ultrasound images, and what this means for clinical decision support systems. Watch this episode on YouTube: https://youtu.be/NuRwJ8dPiJ4 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...

Exploring how LLMs are being used in professional development across healthcare, with implications for training, continuing education, and clinical competency assessment. Watch this episode on YouTube: https://youtu.be/QVpKbIyXdbk 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...