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Google has upgraded its experimental medical chatbot, AMIE, to analyze photos of rashes and interpret a variety of medical imagery, including ECGs and lab result PDFs.
AMIE (Articulate Medical Intelligence Explorer) builds on an earlier version that already beat human doctors in diagnostic accuracy and communication skills. The latest version, powered by Gemini 2.0 Flash, was unveiled in a May 6 preprint published on arXiv.
Why it matters: This represents a step closer to creating an AI medical assistant that thinks like a real doctor. By combining images with clinical data, AMIE mimics how physicians synthesize different types of information to diagnose and treat patients. It could also help mitigate major pain points in healthcare – faster triage, broader access to diagnostic support, and less risk from poor image quality or incomplete patient records.
How it works: The new AMIE model integrates Google’s previous generation of model, Gemini 2.0 Flash with medical-specific reasoning tools:
It can engage in diagnostic conversations, mimicking physician–patient exchanges.
It processes and interprets medical images, even at low quality.
It evaluates lab reports and clinical notes in real time.
It simulates peer review by role-playing all sides of a medical consultation.
To test the upgrade, researchers ran 105 medical scenarios using actors as patients. Each had a virtual consultation with both AMIE and a human doctor. Dermatologists, cardiologists, and internists reviewed the results.
AMIE consistently offered more accurate diagnoses. It also proved more resilient when presented with subpar images, a common issue in real-world telemedicine.
Big picture: With image-processing capabilities and built-in clinical logic, models like AMIE are inching toward becoming full-fledged diagnostic partners.
If you’re thinking about ditching your doctor, I wouldn’t… The tool hasn’t been peer-reviewed and remains experimental. If these results hold, it could reshape how frontline care is delivered – especially where access to human doctors is limited.
AI for Good: Google AI > human doctors?
Source: ChatGPT 4o
Google has upgraded its experimental medical chatbot, AMIE, to analyze photos of rashes and interpret a variety of medical imagery, including ECGs and lab result PDFs.
AMIE (Articulate Medical Intelligence Explorer) builds on an earlier version that already beat human doctors in diagnostic accuracy and communication skills. The latest version, powered by Gemini 2.0 Flash, was unveiled in a May 6 preprint published on arXiv.
Why it matters: This represents a step closer to creating an AI medical assistant that thinks like a real doctor. By combining images with clinical data, AMIE mimics how physicians synthesize different types of information to diagnose and treat patients. It could also help mitigate major pain points in healthcare – faster triage, broader access to diagnostic support, and less risk from poor image quality or incomplete patient records.
How it works: The new AMIE model integrates Google’s previous generation of model, Gemini 2.0 Flash with medical-specific reasoning tools:
It can engage in diagnostic conversations, mimicking physician–patient exchanges.
It processes and interprets medical images, even at low quality.
It evaluates lab reports and clinical notes in real time.
It simulates peer review by role-playing all sides of a medical consultation.
To test the upgrade, researchers ran 105 medical scenarios using actors as patients. Each had a virtual consultation with both AMIE and a human doctor. Dermatologists, cardiologists, and internists reviewed the results.
AMIE consistently offered more accurate diagnoses. It also proved more resilient when presented with subpar images, a common issue in real-world telemedicine.
Big picture: With image-processing capabilities and built-in clinical logic, models like AMIE are inching toward becoming full-fledged diagnostic partners.
If you’re thinking about ditching your doctor, I wouldn’t… The tool hasn’t been peer-reviewed and remains experimental. If these results hold, it could reshape how frontline care is delivered – especially where access to human doctors is limited.