# MedGemma

Tuberculosis still kills an estimated 1.23 million people a year, and high-burden regions lack the radiologists who read chest X-rays. Google Research and Google for Health handed its chest X-ray algorithms to Pretoria-based Nexus AI, which turned them into a CE-certified clinical diagnostic tool now deployed in about 40 healthcare facilities across six countries including South Africa, Sierra Leone and Côte d'Ivoire. More than 90 clinicians have evaluated over 25,000 chest radiographs, cutting turnaround from weeks to under 45 seconds for rapid triage, with final clinical decisions left to health workers.

Tuberculosis still kills an estimated 1.23 million people a year, and high-burden regions lack the radiologists who read chest X-rays. Google Research and Google for Health handed its chest X-ray algorithms to Pretoria-based Nexus AI, which turned them into a CE-certified clinical diagnostic tool now deployed in about 40 healthcare facilities across six countries including South Africa, Sierra Leone and Côte d'Ivoire. More than 90 clinicians have evaluated over 25,000 chest radiographs, cutting turnaround from weeks to under 45 seconds for rapid triage, with final clinical decisions left to health workers.

On 783 de-identified free-text mammography, hip-knee-ankle and brain MR reports, an LLM Council of three general small models plus a chairman reached 83.56% overall accuracy, below the medically trained MedGemma 4B at 88.72%, with the gap driven mainly by hip-knee-ankle classification. A single council member, Qwen3-4B-2507, reached the highest accuracy at 91.69%, above both the council and MedGemma, while the council needed four model calls per report and about 2.44 times MedGemma's peak GPU memory.

On 783 de-identified free-text mammography, hip-knee-ankle and brain MR reports, an LLM Council of three general small models plus a chairman reached 83.56% overall accuracy, below the medically trained MedGemma 4B at 88.72%, with the gap driven mainly by hip-knee-ankle classification. A single council member, Qwen3-4B-2507, reached the highest accuracy at 91.69%, above both the council and MedGemma, while the council needed four model calls per report and about 2.44 times MedGemma's peak GPU memory.

The authors introduce MS-ECG-FM, an ECG foundation model pre-trained by contrastive alignment to several clinical note types — ECG machine reports, cardiologist ECG reports, echocardiography, chest X-ray and discharge summaries; on linear-probe evaluation over 98 labels across multiple datasets it reaches a macro-averaged AUROC of 93.6 versus 91.5 for ECGFounder and 91.2 for MELP, with clear gains on structural heart disease and maintained leadership in single-lead and reduced-lead configurations.

The authors introduce MS-ECG-FM, an ECG foundation model pre-trained by contrastive alignment to several clinical note types — ECG machine reports, cardiologist ECG reports, echocardiography, chest X-ray and discharge summaries; on linear-probe evaluation over 98 labels across multiple datasets it reaches a macro-averaged AUROC of 93.6 versus 91.5 for ECGFounder and 91.2 for MELP, with clear gains on structural heart disease and maintained leadership in single-lead and reduced-lead configurations.