MedFlowBench evaluates VLM agents on full radiology and pathology studies by requiring both task answers and verifiable evidence like key slices and regions of interest, revealing that answer-only scores overestimate performance.
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MONAI: An open-source framework for deep learning in healthcare
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abstract
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare.
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representative citing papers
DiffBCP combines a cumulative shrinkage prior and pre-trained diffusion model prior in Bayesian CP decomposition via a split Gibbs sampler with noise-adaptive coupling, yielding gains on image inpainting and denoising.
A sequential diffusion framework generates controllable abdominal anatomies with a Volume Control Scalar that decouples organ size from body habitus, achieving Dice scores around 0.83 and reducing distributional mismatch by 73.6% in a hepatomegaly example.
Camyla autonomously generates research proposals, experiments, and manuscripts in medical image segmentation, outperforming baselines on 24 of 31 recent datasets while producing 40 human-reviewed papers.
MP-ViT uses dual transformers and cross-attention on axial and sagittal MRI to classify hemorrhages, reporting 5.5% higher AUC than standard ViT and 1.8% higher than CNNs on a dataset of 12,869 subjects.
Tabular clinical data guides contrastive learning on cardiac MR images to build better visual representations by identifying patient similarities, outperforming image-only augmentation on downstream disease prediction tasks.
Proposes a cyclic 2.5D perceptual loss with manufacturer SUVR standardization for T1w MRI to tau PET synthesis, reporting improved regional agreement on ADNI and SCAN cohorts across U-Net, UNETR, SwinUNETR, CycleGAN, and Pix2Pix.
BenchX supplies an 85k-scan benchmark that exposes poor performance of 12 tumor-detection models on underrepresented demographic and protocol subgroups.
CSCS selects initial annotation samples for 3D medical segmentation by combining self-supervised typicality and reconstruction uncertainty through a closed-form pacing rule based on the Difficulty-Coverage Ratio.
SurgVista mitigates spatial interaction incoherence and temporal fidelity collapse in surgical world models through trajectory-based contrastive regularization and drift-perturbed training, outperforming prior methods on a new long-horizon benchmark.
Acquisition parameters like kernel and noise produce axis-specific effects on lung-nodule AI (measurement vs detection) that a 4-feature pixel fingerprint can recover even when DICOM metadata cannot.
GLINT introduces sparsely gated alignment and dense feature regularization on top of DINOv3 and V-JEPA encoders to enable query-specific zero-shot grounding and segmentation in 2D CXR and 3D CT.
Entropy minimization amplifies prediction bias from merged feature clusters under distribution shifts, and DSBR mitigates collapse by equalizing predicted class contributions to the unsupervised loss.
A foundation VAE pretrained on natural images and videos serves as a frozen interface for CT reconstruction, augmentation, and generation, yielding 3.9% NSD gains in segmentation and improved generation metrics across 18 diseases.
A histogram-regularized latent diffusion model synthesizes controllable pulmonary nodules in full 3D CT volumes with improved lesion-level intensity distributions.
VISTA is a test-time adaptation framework for multi-sequence MRI that uses inter-sequence intervention probes and cross-view disagreement variance to gate self-training, yielding Dice gains of +1.89% on low-field African data and +2.82% on pediatric data over the source model.
A teacher–student semi-supervised framework with alignment-preserving patch mixing, position-aware text augmentation, and positional contrastive learning improves medical referring segmentation at low label ratios.
A uniform benchmark across 77 experiments finds SRGAN superior to latent diffusion models for 3D medical image translation, with synthetic volumes indistinguishable from real ones in a 17-physician Turing test.
SIAM achieves state-of-the-art whole-head MRI segmentation of 16 structures including extra-cerebral tissues by training on synthetic data from just six manual templates, matching or exceeding prior methods on 301 scans across eight heterogeneous datasets.
GeoSAE extracts a compact, interpretable feature set from frozen brain MRI foundation models that predicts MCI-to-AD conversion (AUC 0.746) with age-deconfounded annotations and replicates across cohorts.
ESICA delivers state-of-the-art accuracy on a five-modality 3D medical segmentation benchmark while offering a compact variant with far fewer parameters.
A 4D diffusion generative model learns topology-preserving spatiotemporal deformations to synthesize realistic longitudinal brain anatomy trajectories in neurodegenerative diseases from sparse follow-up scans.
DAGMaN uses co-distilled attention-guided masked image modeling with a noisy teacher to enable effective self-supervised pretraining on medical images by selective masking of co-occurring patches and maintenance of attention head diversity, with demonstrations on nodule classification, immunotherapy
Modality gating is backbone-conditional: nnU-Net gates become static priors that degrade ranking score, while Mamba gates retain per-sample routing and improve it; only modality dropout helps both.
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