Abstract Background High-quality clinical documentation is essential for safe, effective care, yet producing it is time consuming and error prone. Large language models (LLMs) can assist with note generation, but clinical adoption is determined by the resulting note quality. However current evaluation practices vary, and their clinical relevance is unclear. Drawing on a multidisciplinary perspective, we examined how quality is assessed and how those assessments align with clinical demands. Methods We systematically searched Ovid Medline and Scopus on 10 April 2025 for peer-reviewed studies that used LLMs in generating clinical notes and included an evaluation of the quality of the resulting text. The screening followed PRISMA and the protocol was preregistered in PROSPERO. Data on metrics, and outcomes were synthesised narratively. Based on these findings, we designed an experimental setup to test the most common evaluation metrics and an LLM-as-evaluator, included for its scalability across large test sets. The experiment used synthetic cases with targeted perturbations. Findings Thirty-seven studies were included. The reporting was dominated by lexical overlap metrics, chiefly ROUGE and BLEU. Semantic similarity metrics, such as BERTScore and BLEURT, were less common. A human evaluation was frequent but heterogeneous, with criteria and methods defined using varying degrees of detail; the most common foci were correctness, fluency, and aspects of clinical acceptability. In our experimental setup, lexical overlap metrics detected deletions and modifications but penalised meaning-preserving paraphrases. Semantic metrics and LLM-as-evaluators were more tolerant of paraphrased perturbations, yet remained sensitive to relevant changes, with performance varying by model and language. Interpretation Current practice relies on lexical overlap metrics that are useful for cursory checks but insufficient as proxies for quality. We recommend a layered strategy that pairs semantic metrics with LLM-as-evaluator for scalability and includes targeted human adjudication. Broader, safety-focused validation across institutions and languages is needed before routine deployment. Funding Business Finland through the GenAID research project. Personal grants listed under Acknowledgments.

OBJECTIVE: The study sought to assist practitioners in identifying and prioritizing radiography exams that are more likely to contain abnormalities, and provide them with a diagnosis in order to manage heavy workload more efficiently (eg, during a pandemic) or avoid mistakes due to tiredness. MATERIALS AND METHODS: This article introduces RTEx, a novel framework for (1) ranking radiography exams based on their probability to be abnormal, (2) generating abnormality tags for abnormal exams, and (3) providing a diagnostic explanation in natural language for each abnormal exam. Our framework consists of deep learning and retrieval methods and is assessed on 2 publicly available datasets. RESULTS: For ranking, RTEx outperforms its competitors in terms of nDCG@k. The tagging component outperforms 2 strong competitor methods in terms of F1. Moreover, the diagnostic captioning component, which exploits the predicted tags to constrain the captioning process, outperforms 4 captioning competitors with respect to clinical precision and recall. DISCUSSION: RTEx prioritizes abnormal exams toward the improvement of the healthcare workflow by introducing a ranking method. Also, for each abnormal radiography exam RTEx generates a set of abnormality tags alongside a diagnostic text to explain the tags and guide the medical expert. Human evaluation of the produced text shows that employing the generated tags offers consistency to the clinical correctness and that the sentences of each text have high clinical accuracy. CONCLUSIONS: This is the first framework that successfully combines 3 tasks: ranking, tagging, and diagnostic captioning with focus on radiography exams that contain abnormalities.

Avoiding obstacles poses a significant challenge for amputees using mechanically-passive transfemoral prosthetic limbs due to their lack of direct knee control. In contrast, powered prostheses can potentially improve obstacle avoidance via their ability to add energy to the system. In past work, researchers have proposed stumble recovery systems for powered prosthetic limbs that provide assistance in the event of a trip. However, these systems only aid recovery after an obstacle has disrupted the user's gait and do not proactively help the amputee avoid obstacles. To address this problem, we designed an adaptive system that learns online to use kinematic data from the prosthetic limb to detect the user's obstacle avoidance intent in early swing. When the system detects an obstacle, it alters the planned swing trajectory to help avoid trips. Additionally, the system uses a regression model to predict the required knee flexion angle for the trip response. We validated the system by comparing obstacle avoidance success rates with and without the obstacle avoidance system. For a non-amputee subject wearing the prosthesis through an adapter, the trip avoidance system improved the obstacle negotiation success rate from 37% to 89%, while an amputee subject improved his success rate from 35% to 71% when compared to utilizing minimum jerk trajectories for the knee and ankle joints.