Large language models (LLMs) hold transformative potential for clinical decision-making in the rapidly advancing field of AI in medicine. This study evaluates how Chain-of-Thought (CoT) prompting techniques affect medical reasoning performance with consideration for clinical applicability. Five LLMs (GPT-4o-mini, GPT-3.5-turbo, o1-mini, Gemini-1.5-Flash, Gemini-1.0-pro) were assessed via API access using CoT prompting methods exhibiting distinct cognitive characteristics. Evaluations included clinical datasets and non-clinical datasets. An iterative QA system was constructed to ensure consistent and reproducible results. o1-mini achieved the highest performance with 88.4 % accuracy in EHRNoteQA using MIMIC-IV discharge summaries. Traditional CoT consistently provided stable results across clinical datasets, while Interactive CoT showed variability - underperforming in complex medical reasoning tasks like MedMCQA (61.7 %) but performing well in clinical notes (83.5 % in EHRNoteQA). Statistical analysis revealed no significant differences between prompting methods across datasets (p > .05), though effect size analysis showed notable variations in method effectiveness. Complex prompting techniques do not significantly enhance performance compared to simpler approaches. Dataset characteristics and model architecture have greater impact, suggesting simpler CoT methods may be more effective for clinical applications.

Early diagnosis of lung cancer to increase the survival rate, which is currently at a low range of mid-30%, remains a critical need. Despite this, multi-omics data have rarely been applied to non-small-cell lung cancer (NSCLC) diagnosis. We developed a multi-omics data-affinitive artificial intelligence algorithm based on the graph convolutional network that integrates mRNA expression, DNA methylation, and DNA sequencing data. This NSCLC prediction model achieved a 93.7% macro F1-score, indicating that values for false positives and negatives were substantially low, which is desirable for accurate classification. Gene ontology enrichment and pathway analysis of features revealed that two major subtypes of NSCLC, lung adenocarcinoma and lung squamous cell carcinoma, have both specific and common GO biological processes. Numerous biomarkers (i.e., microRNA, long non-coding RNA, differentially methylated regions) were newly identified, whereas some biomarkers were consistent with previous findings in NSCLC (e.g., SPRR1B). Thus, using multi-omics data integration, we developed a promising cancer prediction algorithm.

As studies analyzing the networks and relational structures of research topics in academic fields emerge, studies that apply methods of network and relationship analysis, such as social network analysis (SNA), are drawing more attention. The purpose of this study is to explore the interaction of medical education subjects in the framework of complex systems theory using SNA and to analyze the trends in medical education. The authors extracted keywords using Medical Subject Headings terms from 9,379 research articles (162,866 keywords) published in 1963–2015 in PubMed. They generated an occurrence frequency matrix, calculated relatedness using Weighted Jaccard Similarity, and analyzed and visualized the networks with Gephi software. Newly emerging topics by period units were identified as historical trends, and 20 global-level topic clusters were obtained through network analysis. A time-series analysis led to the definition of five historical periods: the waking phase (1963–1975), the birth phase (1976–1990), the growth phase (1991–1996), the maturity phase (1997–2005), and the expansion phase (2006–2015). The study analyzed the trends in medical education research using SNA and analyzed their meaning using complex systems theory. During the 53-year period studied, medical education research has been subdivided and has expanded, improved, and changed along with shifts in society’s needs. By analyzing the trends in medical education using the conceptual framework of complex systems theory, the research team determined that medical education is forming a sense of the voluntary order within the field of medicine by interacting with social studies, philosophy, etc., and establishing legitimacy and originality.

Ha SR, et al. J Adv Prosthodont. 2018 Dec;10(6):395-400. https://doi.org/10.4047/jap.2018.10.6.395