This article examines the integration of OpenAI's Chat Generative Pre-trained Transformer (ChatGPT) into Objective Structured Clinical Examinations (OSCEs) for medical education. OSCEs, essential in evaluating medical trainees, are time and resource-intensive for educators and medical colleges. ChatGPT emerges as a solution, aiding educators in efficient OSCE preparation, including case development, standardized patient training, assessment methods, and grading rubrics. We explore ChatGPT's role in reducing trainee stress through simulated interactions of realistic practice scenarios and real-time trainee feedback. We also discuss the importance of validating ChatGPT outputs for medical accuracy and address compliance concerns. While highlighting ChatGPT's potential in reducing time and cost burdens for educators, we underscore the need for careful and informed application of Artificial Intelligence in medical education. Through examples, we outline ChatGPT's promising future in augmenting medical training and assessment, balancing technological innovation with educational integrity.

Clinical documentation has dramatically changed since the implementation and use of electronic health records and electronic provider documentation. The purpose of this report is to review these changes and promote the development of standards and best practices for electronic documentation for pediatric patients. In this report, we evaluate the unique aspects of clinical documentation for pediatric care, including specialized information needs and stakeholders specific to the care of children. Additionally, we explore new models of documentation, such as shared documentation, in which patients may be both authors and consumers, and among care teams while still maintaining the ability to clearly define care and services provided to patients in a given day or encounter. Finally, we describe alternative documentation techniques and newer technologies that could improve provider efficiency and the reuse of clinical data.

Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine.

Background: Large language models (LLMs) have demonstrated powerful capabilities in natural language tasks and are increasingly being integrated into health care for tasks like disease risk assessment. Traditional machine learning methods rely on structured data and coding, limiting their flexibility in dynamic clinical environments. This study presents a novel approach to disease risk assessment using generative LLMs through conversational artificial intelligence (AI), eliminating the need for programming. Objective: This study evaluates the use of pretrained generative LLMs, including LLaMA2-7b and Flan-T5-xl, for COVID-19 severity prediction with the goal of enabling a real-time, no-code, risk assessment solution through chatbot-based, question-answering interactions. To contextualize their performance, we compare LLMs with traditional machine learning classifiers, such as logistic regression, extreme gradient boosting (XGBoost), and random forest, which rely on tabular data. Methods: We fine-tuned LLMs using few-shot natural language examples from a dataset of 393 pediatric patients, developing a mobile app that integrates these models to provide real-time, no-code, COVID-19 severity risk assessment through clinician-patient interaction. The LLMs were compared with traditional classifiers across different experimental settings, using the area under the curve (AUC) as the primary evaluation metric. Feature importance derived from LLM attention layers was also analyzed to enhance interpretability. Results: Generative LLMs demonstrated strong performance in low-data settings. In zero-shot scenarios, the T0-3b-T model achieved an AUC of 0.75, while other LLMs, such as T0pp(8bit)-T and Flan-T5-xl-T, reached 0.67 and 0.69, respectively. At 2-shot settings, logistic regression and random forest achieved an AUC of 0.57, while Flan-T5-xl-T and T0-3b-T obtained 0.69 and 0.65, respectively. By 32-shot settings, Flan-T5-xl-T reached 0.70, similar to logistic regression (0.69) and random forest (0.68), while XGBoost improved to 0.65. These results illustrate the differences in how generative LLMs and traditional models handle the increasing data availability. LLMs perform well in low-data scenarios, whereas traditional models rely more on structured tabular data and labeled training examples. Furthermore, the mobile app provides real-time, COVID-19 severity assessments and personalized insights through attention-based feature importance, adding value to the clinical interpretation of the results. Conclusions: Generative LLMs provide a robust alternative to traditional classifiers, particularly in scenarios with limited labeled data. Their ability to handle unstructured inputs and deliver personalized, real-time assessments without coding makes them highly adaptable to clinical settings. This study underscores the potential of LLM-powered conversational artificial intelligence (AI) in health care and encourages further exploration of its use for real-time, disease risk assessment and decision-making support.