BACKGROUND: Understanding how clinicians arrive at decisions in actual practice settings is vital for advancing personalized, evidence-based care. However, systematic analysis of qualitative decision data poses challenges. METHODS: We analyzed transcribed interviews with Hebrew-speaking clinicians on decision processes using natural language processing (NLP). Word frequency and characterized terminology use, while large language models (ChatGPT from OpenAI and Gemini by Google) identified potential cognitive paradigms. RESULTS: Word frequency analysis of clinician interviews identified experience and knowledge as most influential on decision-making. NLP tentatively recognized heuristics-based reasoning grounded in past cases and intuition as dominant cognitive paradigms. Elements of shared decision-making through individualizing care with patients and families were also observed. Limited Hebrew clinical language resources required developing preliminary lexicons and dynamically adjusting stopwords. Findings also provided preliminary support for heuristics guiding clinical judgment while highlighting needs for broader sampling and enhanced analytical frameworks. CONCLUSIONS: This study represents the first use of integrated qualitative and computational methods to systematically elucidate clinical decision-making. Findings supported experience-based heuristics guiding cognition. With methodological enhancements, similar analyses could transform global understanding of tailored care delivery. Standardizing interdisciplinary collaborations on developing NLP tools and analytical frameworks may advance equitable, evidence-based healthcare by elucidating real-world clinical reasoning processes across diverse populations and settings.

Performance evaluation and comparison of vision-based automotive modules is a growing need in automotive industry. Off-vehicle evaluation, using a database of video streams offers many advantages over on-vehicle evaluation in terms of reduced costs, repeatability and the ability to compare different modules under the same conditions. An off-vehicle evaluation platform for camera based pedestrian detection is presented, enabling evaluation of industrial modules and internally developed algorithms. In order to maintain a single video database despite variability in camera location and internal parameters, experiments were done with video warping techniques, in which a video is warped to look as if taken from a target camera. To obtain ground truth annotation, both manual and Lidar-based methods were tested. Lidar-based annotation was shown to achieve detection rate >; 80% without human intervention, which can go up to 97.5% using a semi-supervised methodology with moderate human effort. Finally, we examined several performance metrics, and found that the image-based detection criteria used in most of the literature does not fit certain automotive application well. A modified criterion based on real world coordinates is suggested.

We describe a realtime system for finding and tracking unstructured paths in off-road conditions. The system was designed as part of the recent Darpa Grand Challenge and was tested over hundreds of miles of off-road driving. The unique feature of our approach is to combine geometric projection used for recovering Pitch and Yaw with Learning approaches for identifying familiar "drivable" regions in the scene. The region-based component segments the image to "path" and "non-path" regions based on texture analysis borne out of a learning-by-examples principle. The boundary-based component looks for the path bounding lines assuming a geometric model of a planar pathway bounded by parallel edges taken by a perspective camera. The combined effect of both sub-systems forms a robust system capable of finding the path even in situations where the vehicle is positioned out of the path - a situation which is not common for human drivers but is relevant for autonomous driving where the vehicle may find itself occasionally veering out of the path.