For clinics, open models help with clinical text and imaging, visit transcription, patient scheduling and common patient questions. They support doctors and staff and do not replace a diagnosis. Always account for health data protection requirements, the license and whether the model can be deployed on-premises.
FreedomIntelligence (The Chinese University of Hong Kong, Shenzhen) · China
A large family of medical models: chat, an imaging version, the reasoning HuatuoGPT-o1 and the new HuatuoGPT-3 on Qwen3. Does not replace a doctor; decisions are made by a specialist.
AlQuraishi Lab (Columbia University) and the OpenFold consortium · USA
A fully open reproduction of AlphaFold 2 and then AlphaFold 3 under Apache 2.0, with training data. OpenFold3 predicts complexes of proteins, nucleic acids and ligands.
Predicting structures of proteins and ligand complexes
Fine-tuning on the company's own data (training code is open)
An in-house structural analysis service without sending data outside
Open medical models from Swiss EPFL, fine-tuned on clinical guidelines on top of various base models. Does not replace a doctor; decisions are made by a specialist.
Answering staff questions based on clinical guidelines
Meta's models for analyzing people in photos: pose keypoints, body part segmentation, normals and depth. Sapiens2 was trained at high resolution and adds human matting.
EvolutionaryScale / Chan Zuckerberg Biohub (ESM-2 — Meta AI) · USA
Protein language models: they understand amino acid sequences, predict structure (ESMFold2) and help with protein design. Since 2026 all open versions are under MIT.
Protein embeddings for predicting properties (stability, solubility)
Predicting 3D structures of proteins and complexes
Screening enzyme and antibody design candidates before lab work
Qwen3-Embedding models fine-tuned by the startup Octen for search in legal, financial and medical texts. As of January 2026 the 8B version topped the RTEB leaderboard.
Arc Institute (with Together AI, Stanford, NVIDIA) · USA
DNA language models with context up to a million nucleotides: they assess the impact of mutations, annotate genomes and generate sequences. Evo 2 is trained on genomes from all domains of life.
Assessing the likely harmfulness of genetic variants for research
Annotating genomes of microorganisms and plants
Finding promising sequences in breeding and synthetic biology
Google's medical version of Gemma: reads medical texts and images (X-ray, dermatology, histology). A tool for doctors and developers; does not replace a doctor, decisions are made by a specialist.
Draft discharge summaries and reports for a doctor to review
Alibaba's medical model based on Qwen2.5-VL: understands many types of medical images and medical text, and can reason step by step. Does not replace a doctor; decisions are made by a specialist.
Ant Healthcare (Ant Group) and Zhejiang Provincial Medical Information Center · China
A large medical MoE model based on Ling-flash-2.0: 100B parameters with 6B active, so it answers quickly. Does not replace a doctor; decisions are made by a specialist.
First general-purpose Chinese models, then a medical line from 2025. Baichuan-M3 (based on Qwen3-235B) is trained to model a doctor's clinical reasoning.
An open MIT-licensed alternative to AlphaFold 3: predicts structures of protein, DNA and small-molecule complexes; Boltz-2 estimates binding strength, BoltzGen designs new binding proteins.
Predicting how a candidate molecule binds to a target protein
Ranking compounds by predicted binding strength before synthesis
Foundation models that turn an image into a numeric "fingerprint". They are used to build similar-image search, classification and segmentation without large labeled datasets.
Google's lightweight encoder for medical images and text, the same one inside MedGemma. Sorts images and finds similar ones. Does not replace a doctor; decisions are made by a specialist.
Pathology foundation models from France's Bioptimus with 1.1B parameters, plus the compact H0-mini. The first version is open under Apache 2.0. Does not replace a doctor; decisions are made by a specialist.
Histology slide patch features for research models
A prototype for sorting slides by tissue type
Research projects linking morphology and molecular data
University of Sydney and JD Explore Academy · Australia / China
A simple, accurate model for human pose estimation via keypoints. ViTPose++ handles human, animal and whole-body poses; built into the Transformers library.
A foundation model for histology slides: turns patches of digital slides into features for tissue classification. Does not replace a doctor; decisions are made by a specialist.
Research classifiers of tissue types from digital slides
Image-plus-text models for pathology: search slides by an English description, classify without fine-tuning; TITAN describes a whole slide. Does not replace a doctor; decisions are made by a specialist.
Text-query search across a slide archive for research
Draft slide descriptions for research projects
Tissue classification without labels at the start of a study
The reference model for the structure of biomolecules and their complexes. Weights are provided for non-commercial research only; companies need commercial access via Google Cloud or open alternatives (Boltz, OpenFold3).
Academic research on protein and complex structures
Benchmarking open alternatives against the reference on your own targets
Paige's pathology foundation model, trained on millions of digital slides. The first version is Apache 2.0, the second is for research only. Does not replace a doctor; decisions are made by a specialist.
Slide patch features for research classifiers
Selecting slides for re-review in research projects
Comparison with other pathology models on your own archive
Clinical models from Abu Dhabi-based M42, built on Llama and tuned to answer medical questions. Does not replace a doctor; decisions are made by a specialist.
Reference answers to staff on clinical questions
Draft discharge summaries and letters for a doctor to review
Mistral 7B fine-tuned on PubMed Central papers, plus several merges with the general model. Compact and easy to run. Does not replace a doctor; decisions are made by a specialist.
Llama 3 fine-tuned on medical and biological data. One of the first strong open medical models of 2024. Does not replace a doctor; decisions are made by a specialist.
Bo Wang's lab (University of Toronto, Vector Institute) · Canada
An early medical model on Llama 2 70B, trained on dialogues based on medical texts. Now mainly of research interest. Does not replace a doctor; decisions are made by a specialist.
Shanghai Jiao Tong University and Shanghai AI Lab · China
An early general-purpose radiology model: understands 2D and 3D images (CT, MRI) together with text. More of a research base. Does not replace a doctor; decisions are made by a specialist.
