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Healthcare Leader Warns AI Will Dominate Diagnostic Radiology
A leading oncologist urges future radiologists to specialize in interventional procedures due to AI advances in image interpretation.
Harrison.ai Restructures and Launches U.S. AI-Enabled Teleradiology Venture
Harrison.ai lays off staff amid major restructuring and launches Frontier Radiology, an AI-powered U.S. teleradiology business.
UCLA Review Evaluates Breast AI for Early Cancer Detection
A UCLA-led review analyzes current evidence for AI tools in screening mammography, focusing on their capability to detect interval cancers.
From the Research Hub
Human-AI collaboration using GPT-5 for MR grading of endolymphatic hydrops in Ménière's disease.
To evaluate GPT-5 for grading cochlear and vestibular endolymphatic hydrops (EH) on delayed gadolinium-enhanced 3D-FLAIR MRI in Ménière's disease (MD) and to assess prompting strategies and human-AI collaboration. This retrospective study included 436 patients with MD (872 ears). GPT-5 graded cochlear and vestibular EH under multiple prompting strategies and was compared with junior and senior neuroradiologists in independent and GPT-5-assisted workflows. Accuracy (ACC), AUC, F1 score, agreement, and expert-rated clinical usefulness were assessed. Under basic prompting strategies without structured image descriptions, diagnostic performance remained poor: cochlear hydrops grading ACC reached only 25%, and vestibular hydrops grading ACC reached only 39%, despite the inclusion of clinical history or few-shot examples. In contrast, with enhanced prompting that integrated structured image descriptions, clinical context, and representative examples, GPT-5 showed substantially improved performance, achieving accuracies of 87% for cochlear grading and 80% for vestibular grading, with improved agreement and favorable expert ratings. In human-AI interaction experiments, the Human-first mode improved junior physicians' performance, increasing cochlear grading ACC from 36% to 59% and vestibular grading ACC from 42% to 53%. GPT-5 demonstrated limited capability to independently grade EH on MRI when relying primarily on image input. Acceptable diagnostic performance was achieved only after incorporating structured image descriptions, clinical context and representative examples. Under these enhanced prompting conditions, GPT-5 showed potential as an assistive tool for human-AI collaborative interpretation, particularly for less experienced physicians, rather than as a fully autonomous diagnostic system. Question Accurate grading of endolymphatic hydrops on 3D-FLAIR MRI is critical for managing Ménière's disease, but current interpretation lacks standardization and efficiency. Findings GPT-5 showed poor EH MRI grading accuracy without image descriptions, while optimized prompting and human-AI collaboration markedly improved performance toward expert interpretation. Clinical relevance Acceptable MRI grading of endolymphatic hydrops by GPT-5 requires structured image descriptions and contextual prompting. This supports its role as a human-AI collaborative assistive tool, not an autonomous system, potentially reducing reliance on limited medical resources.
Cortical lesions in multiple sclerosis: 25 years of progress, remaining challenges and future directions.
Cortical lesions have evolved from a largely overlooked pathological feature to a central component of multiple sclerosis pathophysiology, yet their biology, clinical relevance and response to treatment remain incompletely understood. In this Review, we trace how pathological studies established the frequency and heterogeneity of cortical demyelination, with particular attention to subpial lesions and inflammation at the brain-cerebrospinal fluid interface. We examine the neuropathological and imaging evidence linking cortical pathology to meningeal inflammation, neuroaxonal injury and progression independent of relapse activity, and assess how emerging magnetic resonance imaging approaches for detecting and monitoring cortical lesions contribute to diagnosis and prognosis across the multiple sclerosis spectrum. Last, we discuss the sensitivity limits of imaging, the effects of disease-modifying therapies on this compartmentalized damage, and how quantitative magnetic resonance imaging, fluid biomarkers and artificial intelligence could help define biologically meaningful cortical endotypes and guide future therapeutic development.
A Specialized Large Multimodal Model for Interpreting PET/CT in Head and Neck Cancer
Background: Diagnosing head and neck cancer using PET/CT is clinically challenging and time-consuming due to the anatomical complexity of the region, motivating computer-aided diagnosis (CAD). Generalist Large Multimodal Models (LMMs) remain limited in medical contexts by insufficient domain-specific knowledge, privacy and security concerns, and verbosity, motivating specialized standalone LMMs. Purpose: We evaluated the feasibility of a specialized LMM for automated PET/CT interpretation in head and neck cancer using a large-scale multi-institutional PET/CT dataset, a tailored training curriculum, and autoregressive training. Methods: LLaVA-NeXT was fine-tuned using a two-level curriculum with image-conversation pairs curated by two radiologists from public data. The dataset included clinically important annotations such as primary tumor presence and metastatic lymph node location. Level 1 used 28,000 image-conversation pairs to learn basic information, including modality type and hypermetabolism. Level 2 used 12,975 pairs to learn primary tumor presence and the existence and anatomical location of cervical lymph node metastases. External validation included four institutions with diverse imaging devices. Results: The specialized LMM substantially outperformed ChatGPT and LLaVA-NeXT. In Level-2 external validation, ROUGE-L, ROUGE-S, Cosine Similarity, Precision, Recall, and F1 were 0.8751, 0.8794, 0.8324, 0.8794, 0.8711, and 0.8751, while generalist models consistently scored below 0.1. Primary tumor classification accuracy was 83.14 +/- 1.15% internally and 69.03 +/- 0.81% externally. For lymph node localization, the corresponding scores were 0.6389, 0.6257, 0.5287, 0.5782, 0.6371, and 0.6648. Conclusion: Specialized LMMs show promising results for fast, accurate PET/CT-based diagnostic support and medical education, highlighting their potential for clinical translation.
From the RadAI FDA Approvals Database
AZmed
Rayvolve AZchest
Rayvolve AZchest is AI-powered software that analyzes chest X-rays to help radiologists detect and highlight key chest abnormalities like pleural effusion, pneumothorax, consolidations, and increased heart size. It uses advanced deep learning algorithms to mark areas of concern, providing extra information to aid diagnosis and workflow, but does not replace the clinician's judgment.
Heartvue.ai, Inc.
Heartvue.Proton
Heartvue.Proton is a software tool that uses artificial intelligence to automatically measure structures in heart MRI scans. It helps doctors analyze the heart and blood vessels more efficiently and accurately, supporting decision-making for patients with cardiovascular disease.
Ceevra, Inc.
Ceevra Reveal 4
Ceevra Reveal 4 is medical imaging software that helps clinicians create and review interactive 3D images from CT, MRI, and PET scans. It uses AI to segment normal anatomy, supporting preoperative planning and aiding surgeons with detailed, visual information about a patient's body.
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