7 articles tagged with #diagnostic-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce MM-NeuroOnco, a large-scale multimodal dataset containing 24,726 MRI slices and 200,000 instructions for training AI models in brain tumor diagnosis. The benchmark reveals significant challenges in medical AI, with even advanced models like Gemini 3 Flash achieving only 41.88% accuracy on diagnostic questions.
Researchers propose INFORM-CT, an AI framework combining large language models and vision-language models to automate detection and reporting of incidental findings in abdominal CT scans. The system uses a planner-executor approach that outperforms traditional manual inspection and existing pure vision-based models in accuracy and efficiency.
Researchers benchmarked 20 multimodal AI models on neuroimaging tasks using MRI and CT scans, finding that while technical attributes like imaging modality are nearly solved, diagnostic reasoning remains challenging. Gemini-2.5-Pro and GPT-5-Chat showed strongest diagnostic performance, while open-source MedGemma-1.5-4B demonstrated promising results under few-shot prompting.
Researchers propose TC-SSA, a token compression framework that enables large vision-language models to process gigapixel pathology images by reducing visual tokens to 1.7% of original size while maintaining diagnostic accuracy. The method achieves 78.34% overall accuracy on SlideBench and demonstrates strong performance across multiple cancer classification tasks.
Researchers developed BUSD-Agent, an AI framework for breast cancer screening that uses cascaded agents and experience-guided decision-making to reduce unnecessary biopsies. The system achieved a 22% reduction in biopsy referrals while improving diagnostic accuracy through retrieval-based learning from past cases.
Researchers developed a neurosymbolic verification framework to audit logical consistency in AI-generated radiology reports, addressing issues where vision-language models produce diagnostic conclusions unsupported by their findings. The system uses formal verification methods to identify hallucinations and missing logical conclusions in medical AI outputs, improving diagnostic accuracy.
AdURA-Net is a new AI framework designed for medical image analysis that addresses uncertainty in clinical decision-making for thoracic disease classification. The system uses adaptive dilated convolution and a dual head loss function to handle uncertain diagnostic labels in medical datasets like CheXpert and MIMIC-CXR.
