In today’s rapidly evolving AI landscape, one persistent challenge is equipping language models with robust decision-making abilities that extend beyond single-turn interactions. Traditional large language models (LLMs) excel at generating coherent responses but often struggle with multi-step problem solving or interacting with dynamic environments. This shortfall largely stems from the nature of the training data,… →
Applying large language models (LLMs) in clinical disease management has numerous critical challenges. Although the models have been effective in diagnostic reasoning, their application in longitudinal disease management, drug prescription, and multi-visit patient care is yet to be tested. The main challenges are limited context understanding across numerous visits, heterogeneous adherence to clinical guidelines, and… →
From business processes to scientific studies, AI agents can process huge datasets, streamline processes, and help in decision-making. Yet, even with all these developments, building and tailoring LLM agents is still a daunting task for most users. The main reason is that AI agent platforms require programming skills, restricting access to a mere fraction of… →
Visual programming has emerged strongly in computer vision and AI, especially regarding image reasoning. Visual programming enables computers to create executable code that interacts with visual content to offer correct responses. These systems form the backbone of object detection, image captioning, and VQA applications. Its effectiveness stems from the ability to modularize multiple reasoning tasks,… →
CONCLUSIONS: Visual dysfunction, particularly a thinner RNFL and lower vessel density, is related to LEDD reduction after STN-DBS. Prolonged administration of dopamine-mimetic drugs prevents visual symptoms. Thus, physicians should consider LEDD adjustment when patients report visual dysfunction before surgery or severe visual symptoms after STN-DBS. →
CONCLUSIONS: The integration of an electronic dashboard to monitor digital social activity in mental health care treatment is novel. This study examines the feasibility and effectiveness of the dashboard and the challenges in implementing this protocol. The lessons learned from developing and implementing the study will inform ongoing discussions about the value of gathering collateral… →
CONCLUSIONS: The findings imply that at the current stage of AI development, people trust human expertise more than accurate AI, especially for decisions traditionally made by humans, such as medical diagnosis, supporting the algorithm aversion theory. Surprisingly, even for highly stigmatized diseases such as AIDS, where we assume anonymity and privacy are preferred in medical… →
CONCLUSIONS AND RELEVANCE: In this cluster randomized clinical trial of risk assessment delivery, POC engagement resulted in a higher rate of assessment of hereditary cancer risk than the DPE approach but a similar rate of genetic testing completion. Using a combination of engagement strategies may be the optimal approach for greater reach and impact. →
CONCLUSIONS: The data show that TASG is a safe and effective therapeutic option for improving the appearance of acne scars and related hyperpigmentation. →
Deep learning faces difficulties when applied to large physical systems on irregular grids, especially when interactions occur over long distances or at multiple scales. Handling these complexities becomes harder as the number of nodes increases. Several techniques have difficulty tackling these big problems, resulting in high computational costs and inefficiency. Some major issues are capturing… →