Increasing Realism and Variety of Virtual Patient Dialogues for Prenatal Counseling Education Through a Novel Application of ChatGPT: Exploratory Observational Study.

BACKGROUND: Using virtual patients, facilitated by natural language processing, provides a valuable educational experience for learners. Generating a large, varied sample of realistic and appropriate responses for virtual patients is challenging. Artificial intelligence (AI) programs can be a viable source for these responses, but their utility for this purpose has not been explored. OBJECTIVE: In this study, we explored the effectiveness of generative AI (ChatGPT) in developing realistic virtual standardized patient dialogues to teach prenatal counseling skills. METHODS: ChatGPT was prompted to generate a list of common areas of concern and questions that families expecting preterm delivery at 24 weeks gestation might ask during prenatal counseling. ChatGPT was then prompted to generate 2 role-plays with dialogues between a parent expecting a potential preterm delivery at 24 weeks and their counseling physician using each of the example questions. The prompt was repeated for 2 unique role-plays: one parent was characterized as anxious and the other as having low trust in the medical system. Role-play scripts were exported verbatim and independently reviewed by 2 neonatologists with experience in prenatal counseling, using a scale of 1-5 on realism, appropriateness, and utility for virtual standardized patient responses. RESULTS: ChatGPT generated 7 areas of concern, with 35 example questions used to generate role-plays. The 35 role-play transcripts generated 176 unique parent responses (median 5, IQR 4-6, per role-play) with 268 unique sentences. Expert review identified 117 (65%) of the 176 responses as indicating an emotion, either directly or indirectly. Approximately half (98/176, 56%) of the responses had 2 or more sentences, and half (88/176, 50%) included at least 1 question. More than half (104/176, 58%) of the responses from role-played parent characters described a feeling, such as being scared, worried, or concerned. The role-plays of parents with low trust in the medical system generated many unique sentences (n=50). Most of the sentences in the responses were found to be reasonably realistic (214/268, 80%), appropriate for variable prenatal counseling conversation paths (233/268, 87%), and usable without more than a minimal modification in a virtual patient program (169/268, 63%). CONCLUSIONS: Generative AI programs, such as ChatGPT, may provide a viable source of training materials to expand virtual patient programs, with careful attention to the concerns and questions of patients and families. Given the potential for unrealistic or inappropriate statements and questions, an expert should review AI chat outputs before deploying them in an educational program.

A multiscale computational model of angiogenesis after traumatic brain injury, investigating the role location plays in volumetric recovery.

Vascular endothelial growth factor (VEGF) is a key protein involved in the process of angiogenesis. VEGF is of particular interest after a traumatic brain injury (TBI), as it re-establishes the cerebral vascular network in effort to allow for proper cerebral blood flow and thereby oxygenation of damaged brain tissue. For this reason, angiogenesis is critical in the progression and recovery of TBI patients in the days and weeks post injury. Although well established experimental work has led to advances in our understanding of TBI and the progression of angiogenisis, many constraints still exist with existing methods, especially when considering patient progression in the days following injury. To better understand the healing process on the proposed time scales, we develop a computational model that quickly simulates vessel growth and recovery by coupling VEGF and its interactions with its associated receptors to a physiologically inspired fractal model of the microvascular re-growth. We use this model to clarify the role that diffusivity, receptor kinetics and location of the TBI play in overall blood volume restoration in the weeks post injury and show that proper therapeutic angiogenesis, or vasculogenic therapies, could speed recovery of the patient as a function of the location of injury.

BurnCare tablet trainer to enhance burn injury care and treatment.

BACKGROUND: Applied Research Associates (ARA) and the United States Army Institute of Surgical Research (USAISR) have been developing a tablet-based simulation environment for burn wound assessment and burn shock resuscitation. This application aims to supplement the current gold standard in burn care education, the Advanced Burn Life Support (ABLS) curriculum. RESULTS: Subject matter experts validate total body surface area (TBSA) identification and analysis and show that the visual fidelity of the tablet virtual patients is consistent with real life thermal injuries. We show this by noting that the error between their burn mapping and the actual patient burns was sufficiently less than that of a random sample population. Statistical analysis is used to confirm this hypothesis. In addition a full body physiology model developed for this project is detailed. Physiological results, and responses to standard care treatment, are detailed and validated. Future updates will include training modules that leverage this model. CONCLUSION: We have created an accurate, whole-body model of burn TBSA training experience in Unreal 4 on a mobile platform, provided for free to the medical community. We hope to provide learners with more a realistic experience and with rapid feedback as they practice patient assessment, intervention, and reassessment.

A Whole-Body Mathematical Model of Sepsis Progression and Treatment Designed in the BioGears Physiology Engine.

Sepsis is a debilitating condition associated with a high mortality rate that greatly strains hospital resources. Though advances have been made in improving sepsis diagnosis and treatment, our understanding of the disease is far from complete. Mathematical modeling of sepsis has the potential to explore underlying biological mechanisms and patient phenotypes that contribute to variability in septic patient outcomes. We developed a comprehensive, whole-body mathematical model of sepsis pathophysiology using the BioGears Engine, a robust open-source virtual human modeling project. We describe the development of a sepsis model and the physiologic response within the BioGears framework. We then define and simulate scenarios that compare sepsis treatment regimens. As such, we demonstrate the utility of this model as a tool to augment sepsis research and as a training platform to educate medical staff.

A Full-Body Model of Burn Pathophysiology and Treatment Using the BioGears Engine.

We have created a model of systemic burn pathophysiology by incorporating a mathematical model of acute inflammation within the BioGears Engine. This model produces outputs consistent with burns of varying severities and leverages existing BioGears functionality to simulate the effect of treatment on virtual patient outcome. The model performs well for standard resuscitation scenarios and we thus expect it to be useful for educational and training purposes.

Open Source Pharmacokinetic/Pharmacodynamic Framework: Tutorial on the BioGears Engine.

BioGears is an open-source, lumped parameter, full-body human physiology engine. Its purpose is to provide realistic and comprehensive simulations for medical training, research, and education. BioGears incorporates a physiologically based pharmacokinetic/pharmacodynamic (PK/PD) model that is designed to be applicable to a diversity of drug classes and patients and is extensible to future drugs. In addition, BioGears also supports drug interactions with various patient insults and interventions allowing for a realistic research framework and accurate dose-patient responses. This tutorial will demonstrate how the generic BioGears PK/PD model can be extended to a new substance for prediction of drug administration outcomes.

A Mathematical Model and MATLAB Code for Muscle-Fluid-Structure Simulations.

This article provides models and code for numerically simulating muscle-fluid-structure interactions (FSIs). This work was presented as part of the symposium on Leading Students and Faculty to Quantitative Biology through Active Learning at the society-wide meeting of the Society for Integrative and Comparative Biology in 2015. Muscle mechanics and simple mathematical models to describe the forces generated by muscular contractions are introduced in most biomechanics and physiology courses. Often, however, the models are derived for simplifying cases such as isometric or isotonic contractions. In this article, we present a simple model of the force generated through active contraction of muscles. The muscles' forces are then used to drive the motion of flexible structures immersed in a viscous fluid. An example of an elastic band immersed in a fluid is first presented to illustrate a fully-coupled FSI in the absence of any external driving forces. In the second example, we present a valveless tube with model muscles that drive the contraction of the tube. We provide a brief overview of the numerical method used to generate these results. We also include as Supplementary Material a MATLAB code to generate these results. The code was written for flexibility so as to be easily modified to many other biological applications for educational purposes.