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AI LLMs notably improve diagnostic accuracy for less experienced brain MRI readers, with diminishing benefits for experts.
AI Concerns Influence Medical Students' Interest in Radiology
AI is deterring a significant portion of medical students from choosing radiology as a career, though most remain optimistic about AI's benefits for the field.
From the Research Hub
Comparative analysis of AI and human radiographer performance in radiographic image assessments: A pilot study using a large language model to simulate radiographer decision-making.
To determine whether a prompting-based, interpretable artificial intelligence (AI) system, specifically a large language model (LLM) that applies structured radiographic criteria derived from radiographer training, can approximate Kuwaiti radiographer acceptance-rejection decisions across varied radiographic examinations and to compare findings with international benchmarks. Thirty anonymized radiographs (chest=3, spine=3, abdomen/KUB=2, upper extremity=15, lower extremity=7) were evaluated by 43 radiographers (1290 decisions) and by an interpretable large language model (LLM) prompted 43 times per image to generate repeated model evaluations under controlled prompting conditions (1290 AI decisions). Decisions were categorized as "keep," "could keep," or "reject." Outcomes were: (i) "keep" vs "reject"; (ii) agreement across cases; (iii) examination-specific trends; and (iv) alignment with expert labels. International comparisons contextualized local thresholds. The AI system applied structured radiographic criteria to descriptive representations of radiographic features rather than directly interpreting image data. Radiographers kept 52.6% of images vs 32.5% for the AI (χ²(1) = 105.6, p < 0.001; OR = 2.30). Case-level agreement was weak (r = 0.16). Radiographers accepted more images across all examinations, with the largest gap in chest radiographs (47 percentage points). Relative to expert labels, the AI aligned more with "reject," while radiographers aligned more with "keep." International comparisons showed that Kuwaiti radiographers applied stricter thresholds than those in European cohorts. The lower acceptance rates and modest agreement indicate that the LLM produces more conservative evaluation outputs than radiographers, particularly in chest examinations. These differences reflect the contextual and experience-based judgments radiographers apply that AI cannot replicate. The international comparison further shows that local decision patterns influence acceptance thresholds and should inform future AI calibration. Although radiographic images were included in input, outputs relied on structured prompting and predefined criteria rather than direct visual interpretation. A prompting-based LLM grounded in radiographer criteria can approximate radiographer decision-making patterns when applied within a structured prompting framework, but remains conservative in the absence of clinical context. These exploratory findings suggest potential applications in radiographer education, quality assurance, and standardization pending further validation. Radiographers decide whether an X-ray image is good enough to keep or needs to be repeated, which affects care quality and safety. This study compared decisions made by radiographers in Kuwait with those from an artificial intelligence tool that follows written image quality rules. This study found that the artificial intelligence rejected more images than radiographers and showed different decision patterns, including differences from experts and from other countries. This matters because understanding these gaps can guide safer use of artificial intelligence in training, quality checks, and consistent imaging decisions.
Whole-body reduced-dose dual-energy CT with deep learning image reconstruction for detection of osteolytic lesions in multiple myeloma.
Deep learning image reconstruction (DLIR) has been incorporated into dual-energy CT (DECT) to improve image quality. However, its applications in reduced-dose DECT for evaluating multiple myeloma remain unclear. This study aimed to evaluate image quality and osteolytic lesion detectability of reduced-dose DECT with DLIR, compared with routine-dose single-energy CT (SECT) with adaptive statistical iterative reconstruction-Veo (ASIR-V). This prospective study enrolled consecutive participants with known or precursor multiple myeloma from July 2023 to October 2024. Each participant underwent whole-body non-contrast SECT (120-kVp images; ASIR-V 40% [AR40]), followed by DECT (74- and 50-keV virtual monochromatic images [VMIs], material decomposition [MD] images; high-strength DLIR [DH]). Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and bone-to-lesion CNR (BLR) were calculated. Three radiologists assessed osteolytic lesion detectability and image quality. Lesion detection performance was analyzed using mixed-effects logistic regression. A total of 43 participants (mean age, 68 ± 10 years; 31 men) with 121 osteolytic lesions were included. DECT reduced radiation dose by 53% versus SECT. Compared with AR40 120-kVp images, DH 74- and 50-keV VMIs exhibited lower and comparable image noise, respectively, and improved CNR, SNR, BLR, and overall image quality (p < 0.05). DH 50-keV VMIs and MD images demonstrated superior lesion conspicuity (p < 0.001), and DH MD images achieved higher sensitivity and specificity than AR40 120-kVp images (p < 0.05). Whole-body reduced-dose multiparametric DECT with DLIR provides improved image quality and osteolytic lesion detectability compared with routine-dose SECT, with a 53% reduction in radiation dose. Multiparametric DECT with DLIR provides a more dose-efficient imaging method for visualizing osteolytic lesions in multiple myeloma compared with standard 120-kVp protocols.
Fully Homomorphic Collaborative Learning for Safe Cross-Healthcare Institution Development and Implementation of Foundation Models
Foundation models (FMs) are powerful tools to allow the broad clinical application of artificial intelligence (AI) in healthcare systems, offering adaptability to different disease, modalities and clinical settings. However, FMs require large-scale datasets to train and fine-tune, while most real-world data are localized in siloed healthcare settings with strict data privacy protection, a restriction that poses a fundamental challenge in the cross-healthcare institution development of FMs. Here, we develop a fully homomorphic collaborative learning framework, named as FOCAL, that enables secure FM-driven diagnosis without exposing raw patient information. Different from traditional federated learning (FL) frameworks that aggregate locally trained models, FOCAL integrates fully homomorphic encryption (FHE) with split training to effectively execute collaborative learning completely over encrypted data. Specifically, we apply FOCAL on different types of retinal and pathology FMs to demonstrate its clinical performance. When facing gradient inversion attacks, FOCAL reduced the data leakage rate from 90.6% to 0% with comparable accuracy performance of the state-of-the-art FL paradigms, owing to the provable security provided by FHE. Moreover, under the same level of security, FOCAL can boost the macro-average AUROC by nearly 50% (from 0.5202 to 0.9831) when evaluated against fully encrypted FL models. In the multi-institution comparative experiments, FOCAL consistently outperforms all single-institution FMs, improving AUROCs by 9.62% and 14.46% on the ocular disease diagnosis and severity classification, respectively. Lastly, external validations on both retinal and pathology FMs further verified the accuracy and security advantages of FOCAL and highlighted its reliable interpretability and generalizability for cross-institution clinical development and implementation of FMs. FOCAL is a novel method to build a secure data-sharing AI community, facilitating healthcare institutions to benefit from and contribute to next-generation FMs development without compromising patient privacy and data security.
From the RadAI FDA Approvals Database
Wuhan United Imaging Healthcare Co.,Ltd
uSONIQUE Genesis, uSONIQUE Genesis Pro, uSONIQUE Genesis Elite, uSONIQUE Genesis Super, uSONIQUE Pulse, uSONIQUE Pulse Pro, uSONIQUE Pulse Elite, uSONIQUE Pulse Super, uSONIQUE Venus, uSONIQUE Venus Pro, uSONIQUE Venus Elite, uSONIQUE Venus Super
The uSONIQUE series by Wuhan United Imaging Healthcare Co., Ltd are imaging systems using pulsed Doppler ultrasound technology. These systems assist clinicians by providing ultrasound imaging capabilities, aiding in the visualization and assessment of various body regions and conditions through sound waves rather than ionizing radiation.
Neurophet., Inc.
Neurophet SCALE PET
Neurophet SCALE PET is an AI-powered software that processes PET imaging scans to assist radiologists in analyzing brain images. It helps clinicians by automating image analysis to improve diagnostic accuracy and efficiency in neurological assessments.
Sonoscape Medical Corp.
Digital Color Doppler Ultrasound System (Autra RS, Autra 90, Autra Senior, Autra CV, e-Autra, Autra 75, Autra 85W, Autra 65, Autra 55, Autra 65i, Autra 90 Elite, Autra 85 Elite, Autra 80 Elite, Autra 75 Elite, Autra 85 CV, Autra 80W, Autra 65 Elite, Autra 55 Elite, Autra 55i, Autra 90 CV, Autra 85, Autra 80, Autra 80 CV, Autra 75 Pro, Autra 75W, Autra 70 Elite, Autra 70, Autra T)
The Digital Color Doppler Ultrasound System by Sonoscape Medical Corp. is an advanced ultrasound device that uses pulsed Doppler technology to create color images showing blood flow and tissue structures. It helps clinicians diagnose and monitor various conditions by providing detailed real-time ultrasound images with color Doppler, assisting in evaluating vascular and organ health.
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