AIBullisharXiv – CS AI · 3d ago7/10
🧠Researchers validated the Melanoscope AI clinical decision support system for skin lesion screening in Russian outpatient settings, achieving 88.6% agreement with expert assessment and zero false negatives among malignant cases. The study introduces quantitative interpretability methods for deep learning models and a three-zone patient routing algorithm, demonstrating the viability of AI-powered dermoscopy as a scalable solution to address dermatologist shortages.
AINeutralarXiv – CS AI · Feb 277/108
🧠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.
AINeutralarXiv – CS AI · May 16/10
🧠A comprehensive review of 55 studies examines AI methods for detecting and diagnosing Major Depressive Disorder, revealing trends toward graph neural networks for brain connectivity analysis, large language models for linguistic data, and multimodal fusion approaches. The survey highlights how AI can address the subjectivity in clinical depression diagnosis while advancing computational psychiatry through improved explainability and fairness.
AIBullishGoogle DeepMind Blog · Apr 306/10
🧠Researchers are developing AI co-clinician systems designed to augment healthcare delivery by partnering artificial intelligence with medical professionals. This initiative explores how AI can enhance clinical decision-making and patient care workflows through collaborative human-AI models rather than full automation.
AIBullisharXiv – CS AI · Apr 156/10
🧠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.
AINeutralarXiv – CS AI · Mar 276/10
🧠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.
🏢 Meta🧠 GPT-5🧠 Gemini
AIBullisharXiv – CS AI · Mar 36/107
🧠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.
AIBullisharXiv – CS AI · Mar 27/1017
🧠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.
AIBullisharXiv – CS AI · Mar 27/1016
🧠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.
AIBullisharXiv – CS AI · Mar 34/106
🧠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.
