Current medical AI articles
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With the US federal government slow to act on AI, states are creating a patchwork of healthcare AI regulations to fill the legislative void.
Original Title: Role of the States in the Future of AI Regulation Journal: JAMA health forum DOI: 10.1001/jamahealthforum.2025.5020 States’ Role in Future AI Regulation Overview A political conflict has emerged in the US between federal and state governments over who should regulate artificial intelligence (AI). After a congressional attempt to place a moratorium on state-level…
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An AI algorithm that analyzes entire coronary arteries via OCT imaging more accurately predicts adverse events than expert analysis of target lesions.
Original Title: Artificial intelligence-based identification of thin-cap fibroatheromas and clinical outcomes: the PECTUS-AI study Journal: European heart journal DOI: 10.1093/eurheartj/ehaf595 AI Identifies High-Risk Plaques to Predict Outcomes Overview This study investigated an artificial intelligence algorithm, called OCT-AID, for its ability to predict future cardiovascular problems. The research was a secondary analysis involving 414 patients who…
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An AI algorithm for coronary imaging standardizes high-risk plaque detection, improving risk prediction when assessing the entire vessel.
Original Title: Artificial intelligence-based identification of thin-cap fibroatheroma: a new paradigm for risk stratification? Journal: European heart journal DOI: 10.1093/eurheartj/ehaf662 AI for Identifying Risky Heart Plaques Overview Atherosclerosis involves the buildup of plaques in arteries, but not all plaques are equally dangerous. Thin-cap fibroatheromas (TCFAs) are considered particularly high-risk and are associated with heart attacks.…
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A metasurface combined with a neural network enables simultaneous detection of frequency, polarization, and intensity for broadband terahertz light.
Original Title: Deep learning-enabled ultra-broadband terahertz high-dimensional photodetector Journal: Nature communications DOI: 10.1038/s41467-025-63364-8 A Deep Learning-Powered THz Photodetector Overview Light carries information in multiple forms, including its intensity, frequency (color), and polarization. Conventional photodetectors typically measure only a subset of these properties, limiting our ability to fully characterize a light field. This paper introduces a…
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Deep indel mutagenesis reveals a checkerboard regulatory architecture for exon splicing and enables prediction of therapeutic oligonucleotide targets.
Original Title: Deep indel mutagenesis reveals the regulatory and modulatory architecture of alternative exon splicing Journal: Nature communications DOI: 10.1038/s41467-025-62957-7 Mapping Splicing Regulation with Indel Mutagenesis Overview Alternative splicing is a fundamental process that allows a single gene to produce multiple protein variants. Errors in this process are linked to numerous human diseases. This study…
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A study of 950 AI medical devices found that lack of clinical validation and public company status were linked to higher odds of early recalls.
Original Title: Early Recalls and Clinical Validation Gaps in Artificial Intelligence-Enabled Medical Devices Journal: JAMA health forum DOI: 10.1001/jamahealthforum.2025.3172 AI Medical Device Recalls and Validation Gaps Overview Artificial intelligence-enabled medical devices (AIMDs) are increasingly common in clinical practice, yet many receive US Food and Drug Administration (FDA) clearance through an accelerated pathway that does not…
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A study of 691 FDA-cleared AI devices found that reporting on efficacy, safety, and bias is inadequate, urging stronger regulatory oversight.
Original Title: Benefit-Risk Reporting for FDA-Cleared Artificial Intelligence-Enabled Medical Devices Journal: JAMA health forum DOI: 10.1001/jamahealthforum.2025.3351 Title FDA Reporting for AI Medical Devices One-Sentence Summary An analysis of 691 FDA-cleared AI medical devices reveals significant gaps in reporting on efficacy, safety, and bias, highlighting a need for improved regulatory oversight. Overview This study investigated the…
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Cancer Detection in Breast MRI Screening via Explainable AI Anomaly Detection
Title AI Anomaly Detection for Breast Cancer MRI One-Sentence Summary This study developed an artificial intelligence model using an anomaly detection approach that improved the accuracy of detecting and localizing breast cancer on MRI scans compared to a standard classification model, especially in realistic low-cancer-prevalence settings. Overview Researchers developed an AI model, Fully Convolutional Data…
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Non-coding genetic elements of lung cancer identified using whole genome sequencing in 13,722 Chinese
Title Lung Cancer’s Non-Coding Genetic Drivers One-Sentence Summary A whole-genome sequencing study of 13,722 Chinese individuals identifies common and rare non-coding genetic variants associated with lung cancer, implicating novel genes and regulatory pathways. Overview This study investigated the genetic basis of lung cancer in the Chinese population, focusing on non-coding regions of the genome that…
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Critical Analysis of Large Language Model-driven Structured Reporting in Thyroid Nodule Diagnosis at US: Challenges and Future Directions
Title LLMs in Thyroid US: Challenges Ahead One-Sentence Summary This letter critiques the use of large language models for thyroid nodule diagnosis from static ultrasound images, highlighting the risk of incomplete feature extraction and oversimplified diagnostic logic, while proposing a shift toward multimodal, dynamic, and collaborative AI systems. Overview This letter to the editor, authored…