Conceptualization of Risk Stratification Using Large Language Models to Predict Severe Mycoplasma pneumoniae Pneumonia

Abstract

Background Large language models (LLMs), such as OpenAI's ChatGPT-4o3 and GPT-5 (Deep Research, Deep Thinking), have emerged as tools with reasonable efficacy in multiple domains of healthcare to enhance literature synthesis, support reasoning, and guide diagnostic and management framework development. In clinical domains, LLMs have been evaluated for readability, accuracy, and decision support, but their role in health professions education scholarship is still emerging. <i>Mycoplasma pneumoniae</i> pneumonia (MPP) is a self-limiting atypical pneumonia; however, a subset of cases progress to severe or necrotizing disease, even in otherwise healthy patients. Predicting such trajectories in immunocompetent adults remains difficult due to the variable nature of host susceptibility and immune factors. This study examines predictive features of severe MPP and explores how LLMs can be leveraged to construct a conceptual risk stratification framework for use in health professions, education scholarship, and research skills development. Methodology We analyzed a single case of necrotizing MPP in a 32-year-old woman to develop a conceptual risk-stratification framework. A structured literature review (2010-2024) was conducted to identify clinical, laboratory, and imaging predictors of severe or refractory MPP. These predictors were compared with the patient's course and organized into a three-tier framework (low, moderate, high risk) using supervised LLM-assisted synthesis. Results The case demonstrated several high-risk predictors of severe MPP, including cavitary disease, a loculated exudative effusion, and pneumothorax. Mapping these findings against literature-derived predictors placed the patient in the high-risk tier of a three-level framework, illustrating how LLM-assisted synthesis can convert a single case into a structured, hypothesis-generating, conceptual risk stratification framework. Conclusions This framework illustrates how LLM-assisted synthesis can link complex case data with published predictors to support early recognition of severe MPP. It also highlights a replicable educational approach for transforming single-patient narratives into hypothesis-generating tools that foster AI literacy and research skills in health professions education. To ensure appropriate interpretive boundaries, we emphasize that the conclusions are theoretical and hypothesis-generating rather than empirically validated. Because alternative interpretations of the literature-derived predictors are possible and the model is grounded in a single case, the framework should not be construed as a generalizable risk tool. Instead, it represents an educational scaffold demonstrating how structured synthesis can support reasoning while acknowledging the need for future empirical evaluation across larger patient samples.

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