I'm excited to kick off a series exploring how Large Language Models (LLMs) and physiological sensors are poised to revolutionize patient care. My previous post highlighted growing sensor capabilities and potential integration with ChatGPT. The next posts will delve into specific ChatGPT Use Cases and research findings.
The Use Cases were based on patients who were being monitored when they became symptomatic. ChatGPT was provided with the symptoms, physiological monitoring data, and healthy baseline data sets as inputs. It was then directed through engineered prompts to generate diagnoses, recommendations, and treatment plans. ChatGPT outputs were compared to diagnoses and treatment strategies developed by the attending doctors, which were presumed to be correct.
My objective is to provide the clinicians in my network with actual cases and ChatGPT results to evaluate and consider. This contrasts with discussions and arguments based on hypothetical scenarios. We are also evaluating the results of our investigations from the perspective of patients who may use ChatGPT as a convenient and inexpensive health consultant to evaluate their symptoms and conditions, provide diagnoses, and offer advice. These uses and their potential benefits and risks are hotly debated topics.
I believe that sharing real-world use cases will fuel useful discussion far more than hypothetical scenarios. I welcome your feedback, questions, and ideas to influence our research.
Let's get the conversation started!
1. How do you envision this technology being integrated into your practice?
2. What concerns do you have about the use of LLMs in healthcare?
3. How do you see the benefits and risks for untrained patients seeking medical advice through LLMs like ChatGPT?
Next Steps
I will be posting ChatGPT use cases involving symptomatic patients who were being continuously monitored and provided respiratory, cardiovascular, and blood glucose data.
Let me know if you have questions and suggestions through comments or directly: ozzie@oprhealth.com.