A5108894488- Artificial Intelligence in Healthcare and Education
- Female Genital Mutilation/Cutting Issues
- Prostate Cancer Diagnosis and Treatment
- Artificial Intelligence in Healthcare
- Clinical Reasoning and Diagnostic Skills
- Prostate Cancer Treatment and Research
- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Radiology practices and education
- Machine Learning in Healthcare
- Autopsy Techniques and Outcomes
University of California, San Francisco
2024
Background Large language models including GPT-4 (OpenAI) have opened new avenues in health care and qualitative research. Traditional methods are time-consuming require expertise to capture nuance. Although large demonstrated enhanced contextual understanding inferencing compared with traditional natural processing, their performance analysis versus that of humans remains unexplored. Objective We evaluated the effectiveness human researchers interviews patients adult-acquired buried penis...
<sec> <title>BACKGROUND</title> Large language models like GPT-4 have opened new avenues in healthcare and qualitative research. Traditional methods are time-consuming require expertise to capture nuance. Although large demonstrated enhanced contextual understanding inferencing compared traditional natural processing, their performance analysis versus that of humans remains unexplored. </sec> <title>OBJECTIVE</title> We evaluated the effectiveness human researchers interviews from patients...
<sec> <title>UNSTRUCTURED</title> Background Manual abstraction of unstructured clinical data is often necessary for granular outcomes research, but time consuming and can be variable quality. Large language models (LLMs) show promise in medical extraction, yet integrating them into research workflows remains challenging poorly described. We developed integrated an LLM-based system automated extraction from electronic health record (EHR) text reports within established database. Methods...