Recent studies highlight the transformative impact of large language models (LLMs) in healthcare, showcasing advancements in predictive modeling, medical imaging, and clinical decision support. Choi et al. (2016) pioneered interpretable healthcare prediction with reverse time attention, while Wang et al. (2022) introduced MedCLIP, merging medical images with textual data through contrastive learning. The emergence of foundation models, like those discussed by Moor et al. (2023), emphasizes their versatility across numerous medical applications. Experimental trials, such as Wan et al.’s (2024) collaboration of nurses with language models, underline practical integration in clinical practice. Moreover, challenges concerning hallucinations and ethical implications of LLM deployments in medicine are being critically analyzed (Thirunavukarasu et al., 2023). Automated systems, including retrieval-augmented generation, are being refined for enhanced diagnostic capabilities (Zhaka et al., 2024). As healthcare technologies evolve, these innovative solutions exhibit potential for significant improvements in patient care and medical research.
Source link
Share
Read more