Опубликована: Янв. 1, 2024
Язык: Английский
European Journal of Investigation in Health Psychology and Education, Год журнала: 2025, Номер 15(1), С. 9 - 9
Опубликована: Янв. 18, 2025
Large language models (LLMs) offer promising possibilities in mental health, yet their ability to assess disorders and recommend treatments remains underexplored. This quantitative cross-sectional study evaluated four LLMs (Gemini 2.0 Flash Experimental), Claude (Claude 3.5 Sonnet), ChatGPT-3.5, ChatGPT-4) using text vignettes representing conditions such as depression, suicidal ideation, early chronic schizophrenia, social phobia, PTSD. Each model’s diagnostic accuracy, treatment recommendations, predicted outcomes were compared with norms established by health professionals. Findings indicated that for certain conditions, including depression PTSD, like ChatGPT-4 achieved higher accuracy human However, more complex cases, LLM performance varied, achieving only 55% while other professionals performed better. tended suggest a broader range of proactive treatments, whereas recommended targeted psychiatric consultations specific medications. In terms outcome predictions, generally optimistic regarding full recovery, especially treatment, lower recovery rates partial rates, particularly untreated cases. While range, conservative highlight the need professional oversight. provide valuable support diagnostics planning but cannot replace discretion.
Язык: Английский
Процитировано
1
Schizophrenia, Год журнала: 2025, Номер 11(1)
Опубликована: Март 20, 2025
Abstract In this proof of concept study, we demonstrated how Large Language Models (LLMs) can automate the conversion unstructured case reports into clinical ratings. By leveraging instructions from a standardized rating scale and evaluating LLM’s confidence in its outputs, aimed to refine prompting strategies enhance reproducibility. Using strategy drug-induced Parkinsonism, showed that LLM-extracted data closely align with rater manual extraction, achieving an accuracy 90%.
Язык: Английский
Процитировано
0
Frontiers in Microbiology, Год журнала: 2025, Номер 15
Опубликована: Янв. 8, 2025
The mortality rate associated with Mycobacterium tuberculosis (MTB) has seen a significant rise in regions heavily affected by the disease over past few decades. traditional methods for diagnosing and differentiating (TB) remain thorny issues, particularly areas high TB epidemic inadequate resources. Processing numerous images can be time-consuming tedious. Therefore, there is need automatic segmentation classification technologies based on lung computed tomography (CT) scans to expedite enhance diagnosis of TB, enabling rapid secure identification condition. Deep learning (DL) offers promising solution automatically segmenting classifying CT scans, expediting enhancing diagnosis. This review evaluates diagnostic accuracy DL modalities pulmonary (PTB) after searching PubMed Web Science databases using preferred reporting items systematic reviews meta-analyses (PRISMA) guidelines. Seven articles were found included review. While been widely used achieved great success CT-based PTB diagnosis, are still challenges addressed opportunities explored, including data scarcity, model generalization, interpretability, ethical concerns. Addressing these requires augmentation, interpretable models, moral frameworks, clinical validation. Further research should focus developing robust generalizable establishing guidelines, conducting validation studies. holds promise transforming improving patient outcomes.
Язык: Английский
Процитировано
0
IGI Global eBooks, Год журнала: 2025, Номер unknown, С. 377 - 392
Опубликована: Март 7, 2025
Governance and ethics in the use of artificial intelligence (AI) healthcare pose fundamental questions to ensure that emerging technologies are implemented a safe, fair, transparent manner. The increasing AI sector offers great opportunities improve patient care, optimize treatments health protocols, increase efficiency products services, but it also raises significant challenges regarding privacy, equity, transparency, accountability. This article aims answer research question: “How important is adopt governance practices ethical precepts systems healthcare?” Through an integrative literature review Scopus Web Science databases, 11 studies were selected present, different ways, solutions for technological innovations benefit patients, professionals, society fair safe
Язык: Английский
Процитировано
0
International Journal of Medical Informatics, Год журнала: 2025, Номер unknown, С. 105886 - 105886
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0
Computers & Geosciences, Год журнала: 2025, Номер unknown, С. 105944 - 105944
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0
Опубликована: Апрель 24, 2025
Язык: Английский
Процитировано
0
SSRN Electronic Journal, Год журнала: 2024, Номер unknown
Опубликована: Янв. 1, 2024
This study evaluates the effectiveness of Artificial Intelligence (AI) in mitigating medical overtreatment, a significant issue characterized by unnecessary interventions that inflate healthcare costs and pose risks to patients. We conducted lab-in-the-field experiment at school, utilizing novel prescription task, manipulating monetary incentives availability AI assistance among students using three-by-two factorial design. tested three incentive schemes: Flat (constant pay regardless treatment quantity), Progressive (pay increases with number treatments), Regressive (penalties for overtreatment) assess their influence on adoption assistance. Our findings demonstrate significantly reduced overtreatment rates—by up 62% conditions where (prospective) physician patient interests were most aligned. Diagnostic accuracy improved 17% 37%, depending scheme. Adoption advice was high, approximately half participants modifying decisions based input across all settings. For policy implications, we quantified (57%) non-monetary (43%) highlighted AI's potential mitigate enhance social welfare. results provide valuable insights administrators considering integration into systems.
Язык: Английский
Процитировано
0
Emergency Care and Medicine, Год журнала: 2024, Номер 1(4), С. 350 - 367
Опубликована: Окт. 12, 2024
Large Language Models (LLMs) are becoming increasingly adopted in various industries worldwide. In particular, there is emerging research assessing the reliability of LLMs, such as ChatGPT, performing triaging decisions emergent settings. A unique aspect emergency process trauma triaging. This requires judicious consideration mechanism injury, severity patient stability, logistics location and type transport order to ensure patients have access appropriate timely care. Current issues overtriage undertriage highlight potential for use LLMs a complementary tool assist more accurate patient. Despite this, remains gap literature surrounding utility process. narrative review explores current evidence implementation Overall, highlights multifaceted applications especially settings, albeit with clear limitations ethical considerations, artificial hallucinations, biased outputs data privacy issues. There room rigorous into refining consistency capabilities ensuring their effective integration real-world improve outcomes resource utilisation.
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
0