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Vaidya et al. introduce MedPAO-VL, replacing opaque end-to-end chest X-ray report generation with a plan-act-observe loop that iteratively reconciles findings against visual evidence.
Ma et al. present MedVTok, a unified medical visual tokenizer pretrained on 28 million images and 70,000 volumes across nine modalities, using slice differential regularization to retain anatomical coherence.
Zhang et al. introduce NPC-RubricBench, evaluating 30 language models across longitudinal oncology care pathways and exposing critical failure mode shifts from diagnostic commission to treatment omission.
Huang et al. introduce HAD-VLM, a unified vision-language framework combining autoregressive text with continuous diffusion prediction to eliminate quantization errors in clinical ultrasound and X-ray measurements.
Evaluating 8B-27B open models on 1,552 official residency exam questions shows that while raw scores qualify models for 43 specialties, medical fine-tuning harms calibration and multimodal models ignore clinical images.
Evaluating six 7-9B models across six categories of clinical harm reveals that risk-bearing language is statistically independent of factuality, with domain adaptation suppressing drug mentions while amplifying overconfidence.
Across 27 frontier models, conversational health assistants frequently validate mistaken user self-diagnoses instead of issuing critical clinical warnings when life-threatening conditions remain possible.
By pairing noisy-OR pooling for supported findings with log-sum-exp suppression for unsupported alternatives, Diagnostic Loss eliminates spurious hallucinations without altering model architectures.
A standardized benchmark spanning 13 imaging sources and 2,000 compounds shows that while test-time adaptation mitigates domain shift, AI models degrade to chance level on rare phenotypic profiles.
An empirical audit of 3D shape completion foundation models reveals that despite visual plausibility, zero-shot reconstructions penetrate patient bone and achieve a 0% clinical implantability rate.
By embedding physical voxel dimensions and geometric constraints directly into spatial embeddings and attention, MAGEFormer eliminates resampling artifacts and sets new boundary accuracy baselines on abdominal CT.
By applying mechanistic interpretability to chest X-ray classifiers, researchers identified dedicated attention heads that latch onto corner text rather than lung pathology, a bias completely invisible to standard output ablation.
Systematic ablations across public benchmarks demonstrate that while stage-redacted reports add genuine prognostic survival signal, slide-level VQA is ungrounded (74.8% accuracy with slide vs 74.4% without) and zero-shot prompts do not outperform linear probes.
Comparing nine imputation methods on 40,336 ICU records reveals that pipelines with indistinguishable AUROC produce divergent high-risk patient alert lists, with Jaccard overlaps as low as 0.518.
Attenuating high-evidence spatial nodes and calibrating predicted grade distributions based on counterfactual response stability delivers robust single-image test-time adaptation across five clinical fundus datasets without model updates.
A 300-case evaluation of role-specific virtual specialist consiliums shows structured multidisciplinary synthesis consistently outperforms standalone opinions, with GPT-5.6-Sol leading overall clinical quality and consensus speed.
With only 10,550 parameters per model, Evidence Field couples spatial image measurements with replayable covering programs, surpassing standard CNN and ViT baselines across held-out hospital sites on CAMELYON17-WILDS.
Conditioning lifespan normative models of cortical thickness on subcortical anatomy enables site calibration with 20 scans and reveals structural dissociations predictive of conversion to dementia.
Formulating adaptive therapy through robust contextual reinforcement learning yields treatment schedules that Pareto-dominate clinical heuristics and maintain efficacy despite noisy biomarker measurements.
Modeling dominant-label pathology annotations via right-censored likelihood and severity-ordered residual routing preserves subtle secondary lesion findings suppressed by standard multiclass MIL.
Pretrained on synthetic datasets with a flexible proportional-odds prior and quantile regression loss, SurvICL outperforms 20 baselines and tops the 50-year-old Cox model on small cohorts.
Evaluating five frozen neuroimaging foundation encoders on longitudinal cohorts demonstrates that released embeddings permit up to 99% rank-1 patient re-identification and over 90% recorded-sex inference.
Coupling a 3D rectified flow vascular refiner with a slice-wise diffusion decoder resolves vessel hallucination and fragmentation, reducing disconnected vascular components by over 20%.
Combining frozen 3D foundation model features with GPU-accelerated iterative optimization and lesion-volume preservation wins the Learn2Reg 2026 PSMAReg Challenge with zero task-specific training.
An audit across 1,112 ABIDE I brain scans and seven harmonization methods shows frozen site classifiers swing by 46 points across seeds, fall to chance under simple blur, and miss residual scanner signals.
Conditioning on paired text and 1.8M structures yields 15 of 18 property wins, higher binding confidence on 13 of 20 targets, and a 107 nM wet-lab binder.
Replacing uniform random cropping with coarse-to-fine anatomical probes eliminates observation waste and matches full-training transfer at 48% of SSL epochs.
Weighted conformal prediction with directional, class-specific bounds keeps nominal 90% coverage on BraTS 2020 while narrowing intervals by 8 to 14%.
A multi-agent framework derives the benchmark specification itself, reaching 90.9% Task-Space F1 and 994 of 1,000 sampled items passing human audit.
Diffusion-based detectors reach only 0.56 to 0.72 pixel-level AUROC and PatchCore variants 0.76 to 0.83 when anatomy, views, and protocols vary.
TAP-JEPA, a ViT-B/3D trained on 113,611 CT volumes, is audited to show that masked prediction learns anatomy well but pulls sporadic pathology toward normal.
Dutta et al. couple 458 DNABERT models with deterministic biological verifiers, preventing LLM hallucinations when prioritizing noncoding single nucleotide variants.
He et al. introduce bidirectional closed-form continuous-time modules to model irregular cross-slice medical imaging, outperforming RWKV and GRU baselines with lower latency and peak memory.
Lee et al. introduce Missing Modality-Aware Local Temperature Scaling, calibrating voxel-wise brain tumor segmentation confidences when MRI acquisition sequences are incomplete.
Cheng et al. evaluate nine computational pathology vision-language models across pathologist-validated phrasing, uncovering high sensitivity to clinical paraphrases and diagnostic competition.
Ma et al. introduce trajectory-based reinforcement learning for clinical decision making, finding that selective diagnostic acquisition outperforms full-modality inputs.
A coordinate-based multimodal fusion architecture that maps available clinical, imaging, and fluid biomarkers into an interpretable RPNS space without synthetic data imputation.
A post-hoc calibration technique using intra-slide internal controls to align gigapixel pathology embeddings across scanner hardware without retraining the backbone.
Model-agnostic calibration for proprietary 3D CT models using test-time geometric perturbations and confidence-gated blending, cutting calibration error up to 54%.
Extending medical triage beyond binary autonomous vs defer decisions: a framework that routes cases to autonomous prediction, human clinician, or human with AI guidance.
A medical vision-language model trained on ~40k cases to classify all 12 WHO 2021 brain tumor types, with uncertainty and report generation, plus multi-reader and prospective checks.
A training-free agent that starts from sparse HPO-like phenotypes, keeps a probabilistic disease ranking, and picks informative follow-ups, with large Recall@1/5 gains on four cohorts.
DeepMind’s follow-on to AlphaGenome: a 1 PB catalogue of single-nucleotide variant molecular predictions, plus an AVI ranking score that blends regulatory and missense signals for research use.
DeepMind’s Nature paper on a unifying DNA model: 1 Mb context, base-resolution multimodal tracks, and strong regulatory variant-effect scores, the positive-science sequel to AlphaFold, with clear limits.
A medical safety/eval note: three-way retain/suppress/defer triage on VTaC with a finite-sample style bound on silenced true alarms, and a clear warning about multiplicity eating the certificate.
An eval methods brief: LoRA-tuned PrecepTron plus GRAND-ROUNDS (9,217 physician scores) aim for physician-level grading of open-ended clinical LLM answers, including piecemeal case presentation.
A stopping layer that tries to decide when a sequential diagnosis agent should diagnose or defer: with finite-sample style tests, and an honest ‘exploratory, not confirmatory’ framing.
A medical imaging methods note: small pulmonary nodules show a Gaussian radial intensity prior; GRIPNet designs every module around that physics and reports strong mAP@0.5 on three public CT benchmarks.
A methods brief on patient-specific EHR concept encoding: RL picks how much KG context each code gets, a GNN + frozen LLM refine representations, reported gains on MIMIC-III/IV backbones.
Self-hosted Llama extracts structured T-stage from radiology reports with source-text links: 90% accuracy vs a four-expert reference on 130 reports.
A living eval suite of 43 biomedical HDLSS datasets across 28 feature×sample operating points: tabular foundation models lead at the reference cell, but logistic regression stays uncomfortably close.
Multicenter retrospective and prospective evaluation of an AI-assisted clinical trial recommender: ~91% top-10 hit rate vs clinician picks, 55% less screening time, and more trial access in a live tumor board.