DALPHIN benchmark finds the pathology-specific AI copilot PathChat+ shows no statistically significant difference from expert pathologists in 4 of 6 tasks, with general models matching in 1-2 tasks, on a diverse open dataset released for ongoing evaluation.
citation dossier
Pathvqa: 30000+ questions for medical visual question answering
18Pith papers citing it
18reference links
cs.CVtop field · 14 papers
UNVERDICTEDtop verdict bucket · 16 papers
why this work matters in Pith
Pith has found this work in 18 reviewed papers. Its strongest current cluster is cs.CV (14 papers). The largest review-status bucket among citing papers is UNVERDICTED (16 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
representative citing papers
PlantInquiryVQA shows multimodal LLMs describe plant symptoms but struggle with clinical reasoning and diagnosis, with structured Chain of Inquiry improving correctness and reducing hallucinations.
KIRA is a unified architecture for visual RAG that reports 0.97 retrieval precision, 1.0 grounding, and 0.707 domain correctness across medical, circuit, satellite, and histopathology domains via hierarchical chunking, dual-path retrieval, and evidence-conditioned generation.
A new multi-frame VQA benchmark on volumetric MRI demonstrates that bounding-box supervised fine-tuning improves spatial grounding in VLMs over zero-shot baselines.
Instruction-tuned vision-language model PaveGPT, trained on a large unified pavement dataset, achieves substantial gains over general models in comprehensive, standard-compliant pavement condition assessment.
VISTA uses prefix resampling and a vision-aware attention score to address data imbalance and language prior bias in self-improvement training of MLLMs, yielding up to 13.66% gains on reasoning tasks.
Self-verification in medical VQA creates a verification mirage where verifiers exhibit high error and agreement bias on wrong answers, with reliability strongly conditioned on task type.
RadThinking releases a large longitudinal CT VQA dataset stratified into foundation perception questions, single-rule reasoning questions, and compositional multi-step chains grounded in clinical reporting standards for cancer screening.
MedSynapse-V evolves latent diagnostic memories via meta queries, causal counterfactual refinement with RL, and dual-branch memory transition to outperform prior medical VLM methods in diagnostic accuracy.
DCI unifies backdoor adjustment and instrumental variable learning in MedVQA to extract deconfounded representations, yielding better out-of-distribution performance on SLAKE, VQA-RAD and similar benchmarks.
Dialectic-Med uses proponent-opponent-mediator agents with visual falsification to enforce grounded diagnostic reasoning in MLLMs, achieving SOTA accuracy and reduced hallucinations on MIMIC-CXR-VQA, VQA-RAD, and PathVQA.
A trajectory-aware process reward using DTW on sentence embeddings, combined with exact-match in GRPO after SFT, raises mean medical VQA accuracy from 0.598 to 0.689 across six benchmarks.
InternVL 2.5 is the first open-source MLLM to surpass 70% on the MMMU benchmark via model, data, and test-time scaling, with a 3.7-point gain from chain-of-thought reasoning.
LiteMedCoT-VL distills chain-of-thought from a 235B model to 2B VLMs via LoRA, reaching 64.9% accuracy on PMC-VQA and beating a 4B zero-shot baseline by 11 points.
A Medical Entity Tree organizes medical knowledge to engineer higher-quality training data that boosts general MLLMs on medical benchmarks.
BiasCareVL is a bias-aware vision-language framework trained on 3.44 million medical samples that outperforms prior methods on clinical tasks like diagnosis and segmentation while aiming for equitable performance under data imbalances.
MAny addresses dual-forgetting in multimodal continual instruction tuning via CPM and LPM merging strategies, delivering up to 8.57% accuracy gains on UCIT benchmarks without additional training.
citing papers explorer
-
DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset
DALPHIN benchmark finds the pathology-specific AI copilot PathChat+ shows no statistically significant difference from expert pathologists in 4 of 6 tasks, with general models matching in 1-2 tasks, on a diverse open dataset released for ongoing evaluation.
-
Thinking Like a Botanist: Challenging Multimodal Language Models with Intent-Driven Chain-of-Inquiry
PlantInquiryVQA shows multimodal LLMs describe plant symptoms but struggle with clinical reasoning and diagnosis, with structured Chain of Inquiry improving correctness and reducing hallucinations.
-
KIRA: Knowledge-Intensive Image Retrieval and Reasoning Architecture for Specialized Visual Domains
KIRA is a unified architecture for visual RAG that reports 0.97 retrieval precision, 1.0 grounding, and 0.707 domain correctness across medical, circuit, satellite, and histopathology domains via hierarchical chunking, dual-path retrieval, and evidence-conditioned generation.
-
Beyond a Single Frame: Multi-Frame Spatially Grounded Reasoning Across Volumetric MRI
A new multi-frame VQA benchmark on volumetric MRI demonstrates that bounding-box supervised fine-tuning improves spatial grounding in VLMs over zero-shot baselines.
-
Vision-Language Foundation Models for Comprehensive Automated Pavement Condition Assessment
Instruction-tuned vision-language model PaveGPT, trained on a large unified pavement dataset, achieves substantial gains over general models in comprehensive, standard-compliant pavement condition assessment.
-
Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training
VISTA uses prefix resampling and a vision-aware attention score to address data imbalance and language prior bias in self-improvement training of MLLMs, yielding up to 13.66% gains on reasoning tasks.
-
Verification Mirage: Mapping the Reliability Boundary of Self-Verification in Medical VQA
Self-verification in medical VQA creates a verification mirage where verifiers exhibit high error and agreement bias on wrong answers, with reliability strongly conditioned on task type.
-
RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology
RadThinking releases a large longitudinal CT VQA dataset stratified into foundation perception questions, single-rule reasoning questions, and compositional multi-step chains grounded in clinical reporting standards for cancer screening.
-
MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution
MedSynapse-V evolves latent diagnostic memories via meta queries, causal counterfactual refinement with RL, and dual-branch memory transition to outperform prior medical VLM methods in diagnostic accuracy.
-
Dual Causal Inference: Integrating Backdoor Adjustment and Instrumental Variable Learning for Medical VQA
DCI unifies backdoor adjustment and instrumental variable learning in MedVQA to extract deconfounded representations, yielding better out-of-distribution performance on SLAKE, VQA-RAD and similar benchmarks.
-
Dialectic-Med: Mitigating Diagnostic Hallucinations via Counterfactual Adversarial Multi-Agent Debate
Dialectic-Med uses proponent-opponent-mediator agents with visual falsification to enforce grounded diagnostic reasoning in MLLMs, achieving SOTA accuracy and reduced hallucinations on MIMIC-CXR-VQA, VQA-RAD, and PathVQA.
-
Improving Medical VQA through Trajectory-Aware Process Supervision
A trajectory-aware process reward using DTW on sentence embeddings, combined with exact-match in GRPO after SFT, raises mean medical VQA accuracy from 0.598 to 0.689 across six benchmarks.
-
Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
InternVL 2.5 is the first open-source MLLM to surpass 70% on the MMMU benchmark via model, data, and test-time scaling, with a 3.7-point gain from chain-of-thought reasoning.
-
LiteMedCoT-VL: Parameter-Efficient Adaptation for Medical Visual Question Answering
LiteMedCoT-VL distills chain-of-thought from a 235B model to 2B VLMs via LoRA, reaching 64.9% accuracy on PMC-VQA and beating a 4B zero-shot baseline by 11 points.
-
Learning from Medical Entity Trees: An Entity-Centric Medical Data Engineering Framework for MLLMs
A Medical Entity Tree organizes medical knowledge to engineer higher-quality training data that boosts general MLLMs on medical benchmarks.
-
Bias-constrained multimodal intelligence for equitable and reliable clinical AI
BiasCareVL is a bias-aware vision-language framework trained on 3.44 million medical samples that outperforms prior methods on clinical tasks like diagnosis and segmentation while aiming for equitable performance under data imbalances.
-
MAny: Merge Anything for Multimodal Continual Instruction Tuning
MAny addresses dual-forgetting in multimodal continual instruction tuning via CPM and LPM merging strategies, delivering up to 8.57% accuracy gains on UCIT benchmarks without additional training.
- MMRareBench: A Rare-Disease Multimodal and Multi-Image Medical Benchmark