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🗓️ 2025 in Review — Foundation Models in Medicine

Compact conclusion of 43 digested 2025 papers.

The year in one paragraph

Foundation models in medicine matured in 2025 from a collection of single-modality encoders into a contested design space spanning vision-only self-supervised backbones, medical vision-language models (VLMs), and unified generalist systems with pixel-level grounding. The year's central tension is specialist vs. generalist: domain-specific medical pretraining (RETFound, MedSAM, CT-FM) still wins on data efficiency and fine-grained tasks, but rapidly scaling general-purpose encoders (DINOv2/v3, SigLIP2, video LVMs) increasingly close the gap, and several benchmarks now show no single model dominates. Three forces shape almost every paper: the impossibility of centralizing patient data (driving federated, model-merging, and continual-learning approaches), the scarcity of labeled volumetric/3D data (driving SSL paradigms like JEPA and SimCLR), and the demand for clinical trust (driving grounding, uncertainty, robustness, and prospective validation). By year's end the frontier had shifted toward 3D/volumetric models trained on uncurated health-system data, unified perception-plus-reasoning VLMs, and rigorous benchmarking that interrogates how FMs are adapted and evaluated rather than chasing single-task SOTA.

🕒 Timeline

  • 2025-01: Native-3D vision-only CT encoders arrive — CT-FM (148k scans, intra-sample contrastive) and MEDFORM (CT+clinical-tabular contrastive) establish vision-centric, non-text 3D pretraining.
  • 2025-02: Privacy-driven composition emerges — MedForge (open-source-style LoRA merging), Med-LEGO (training-free model fusion), MedTok (graph-aware EHR tokenizer), and RELICT (memorization/replica auditing).
  • 2025-03: Generalist-via-adaptation peaks — continual-learning segmentation (CL-Net, 235 anatomies), unified ReID (MaMI), few-shot domain adaptation (MFM-DA), and the CUPCase finding that GPT-4o beats clinical LLMs on rare cases.
  • 2025-04: Self-supervised paradigm innovation — CheXWorld brings JEPA world-modeling to radiographs; multi-teacher distillation agglomerates heavy FMs into lightweight students.
  • 2025-05: Scaling laws and generalist-VLM adaptation — BioVFM-21M characterizes SSL scaling; MedBridge repurposes frozen general VLMs via MoE; XMedGPT adds grounding/uncertainty/prognosis.
  • 2025-06–07: Multimodal multi-disease vision FMs (MerMED-FM) and a turn toward trustworthiness — de-identification (DCM-DeID), latent-fragility auditing (LAPD).
  • 2025-08–09: Benchmarking-as-contribution and continual learning consolidate — generalist-vs-specialist studies (ocular), data scaling laws for radiology, UNICON/EWC-diffusion-replay, and Citrus-V unified grounding.
  • 2025-10: External-validity reckoning — CardioBench and ProstNFound+ (first prospective FM validation) test whether FMs survive "beyond the lab"; video models probed as zero-shot medical learners.
  • 2025-11: Health-system-scale 3D FMs (NeuroVFM/Vol-JEPA, 5.24M volumes) and unified grounded VLMs (UMind-VL) define the frontier; comparative encoder benchmarks proliferate.
  • 2025-12: Generalist 3D-via-2D adaptation (AnyMC3D), retrieval-guided continual learning (PRIMED), surgical FMs (LapFM), and FMs used as semantic priors inside other pipelines (PGMP).

📌 Key papers

  • Vision Foundation Models for Computed Tomography (CT-FM) (2025-01, Arxiv) — established native-3D, vision-only CT pretraining via intra-sample contrastive learning, arguing text supervision degrades fine-grained visual features.
  • MedForge (2025-02, Arxiv) — reframed FM-building as asynchronous open-source LoRA contribution, a template for privacy-preserving, decentralized model composition.
  • CL-Net (2025-03, Arxiv) — continual-learning segmentation of 235 whole-body anatomies across 36 partially-labeled datasets, beating nnUNet ensembles and SAM-style FMs at 55.5 MB.
  • CUPCase (2025-03, AAAI 2025) — real-world rare-case benchmark showing general GPT-4o beats dedicated clinical LLMs, puncturing exam-benchmark optimism.
  • CheXWorld (2025-04, CVPR 2025) — first JEPA world-model for radiographs, matching/beating large FMs with 20× less compute.
  • BioVFM-21M (2025-05, Arxiv) — first systematic SSL scaling-law study for medical vision; data diversity > raw volume, benefits are task-dependent.
  • MedBridge (2025-05, Arxiv) — repurposes frozen general-purpose VLMs for multi-label diagnosis via query injection + MoE, exemplifying adapt-don't-retrain.
  • MerMED-FM (2025-06, Arxiv) — vision-only multimodal/multi-disease FM (7 modalities, 0.935 mean AUROC) via memory-augmented teacher-student SSL.
  • Embeddings to Diagnosis (LAPD) (2025-07, Arxiv) — showed clinical LLMs flip diagnoses under realistic perturbations while surface metrics stay high, defining latent-robustness as a problem.
  • Citrus-V (2025-09, Arxiv) — unified medical FM merging detection, segmentation, and multimodal CoT reasoning with pixel-level grounding.
  • Data Scaling Laws for Radiology Foundation Models (2025-09, Arxiv) — controlled CLIP-vs-DINOv2 comparison; CLIP scales ~3× faster on findings, DINO dominates lines & tubes, motivating center-specific continual pretraining.
  • CardioBench (2025-10, Arxiv) — standardized echo benchmark revealing no model dominates and general encoders (SigLIP2, DINOv3) are competitive.
  • ProstNFound+ (2025-10, Arxiv) — first prospective clinical validation of an adapted medical FM, showing no degradation at a new site five years later.
  • Health system learning achieves generalist neuroimaging models (NeuroVFM) (2025-11, Arxiv) — Vol-JEPA on 5.24M uncurated clinical volumes; annotation-free 3D FM beating GPT-5 on report generation, defining "health system learning."
  • UMind-VL (2025-11, Arxiv) — first ultrasound FM unifying pixel-level grounded perception with clinical reasoning in one VLM.
  • Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification (AnyMC3D) (2025-12, Arxiv) — showed adapted frozen 2D FMs beat 3D-native medical FMs and exposed three pervasive FM-benchmarking pitfalls.
  • PRIMED (2025-12, Arxiv) — retrieval-guided continual learning (18M-entry RAG memory) for generalist medical VLMs, advancing privacy-preserving, forgetting-resistant updates.

➡️ Read the full trend analysis