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AI-MARRVEL is an AI system for genetic diagnosis that improves diagnostic accuracy, surpassing state-of-the-art benchmarked methods.
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Recommendations for analysis of pathology image search algorithms and a road map for clinical adoption are provided.
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The performance of four histopathology slide search engines — Yottixel (“one yotta pixel”), SISH (self-supervised image search for histology), HSHR (High-Order Correlation-Guided Self-Supervised Hashing-Encoding Retrieval), and RetCCL (Retrieval with Clustering-guided Contrastive Learning) — on three patients’ cases was found to inconsistently deliver reliable and accurate results. The authors suggest a minimal set of necessary improvements to foster clinical adoption of histopathology slide search engines in the future.
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Tokenization algorithms may to be blame when generative large language models inconsistently match medical billing codes to their preferred code descriptions.
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In the most comprehensive head-to-head comparison of modern AI-based large language models (LLMs) for application in oncology, a significant heterogeneity in model accuracy was observed, with GPT-4 showing performance competitive with a human benchmark. Despite this state-of-the-art performance, all models exhibited clinically significant error rates, with many incorrect responses given confidently and consistently across repeated prompts, underscoring the current limitations of LLMs as reliable reference tools for patients and medical professionals in the domain of oncology.
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AI Grand Rounds features informal conversations with a variety of unique experts exploring the deep issues at the intersection of artificial intelligence, machine learning, and medicine.
NEJM AI
Vol. 1 No. 5 | May 2024
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The complexity of achieving reimbursement for artificial intelligence (AI) in the U.S. health care system is used as an example to illustrate the roles of the various stakeholders, how reimbursement mechanisms for both fee-for-service (FFS) and value-based care (VBC) work, and how sustainable reimbursement for both FFS and VBC has been achieved through a real-world example of an autonomous AI for diabetic retinal examinations.
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A nationwide study found that an artificial intelligence language model outperformed physicians in medical board residency examinations across multiple specialties.
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