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The Segment Anything Model (SAM), introduced by Meta AI, represented a significant leap forward in image segmentation technology [3]. Trained on over a billion masks, SAM demonstrated remarkable versatility in segmenting a wide array of objects across various domains.
JMIR AI 2025;4:e72109
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These AI values were then summed across all relevant user needs to compute the weighted score (WS)=sum of absolute importance value, ranking the significance of each feature of the “WHATs” within the overall app structure. All these were ranked to highlight the most essential components of the MAwar application [21]. These analytical steps provided a detailed, quantified overview of the key priorities for the MAwar application’s development.
JMIR Form Res 2025;9:e65542
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With the app for HCPs, they can (1) check, modify, or confirm the artificial intelligence (AI)–driven tailored exercise prescription and send it to patients; and (2) check the feedback information from patients and optimize the exercise prescription dynamically.
JMIR Mhealth Uhealth 2025;13:e60115
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Although this study analyzed text rather than images, CLAIM was followed because it is an established guideline for AI-based research in radiology and is deemed appropriate for NLP [15-17].
The proposed algorithm is illustrated in Figure 1. Using the LLM, key lung cancer findings were extracted from radiology reports and quantified to obtain structured data. The structured data were subsequently used for clustering.
Flowchart of radiology reports clustering using LLM. LLM: large language model.
JMIR Cancer 2025;11:e57275
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