Can ChatGPT See My Patient? An Introduction To Generative AI and Large Language Models in Care Settings-View Presentation

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The panel discussed the implications and challenges of using AI, particularly large language models like ChatGPT, in clinical settings. The consensus was that while AI technology shows promise, it is not yet capable of replacing human clinicians in patient care. Various experts highlighted key concerns such as potential bias, privacy issues, and inaccuracies that could arise from AI-generated data, especially in sensitive areas like mental health diagnosis and treatment.<br /><br />The discussion emphasized that current AI models largely draw on existing social media and internet content, which may perpetuate biases and inaccuracies. For instance, previous attempts to generate images or make decisions based on AI have revealed ingrained stereotypes and prejudices. There's a significant risk that AI could escalate misinformation unless it is trained with more precise, high-quality clinical data.<br /><br />The panel advocated for a cautious and ethical approach to deploying AI technologies, ensuring compliance with privacy standards like HIPAA and structurally addressing any biases. They also recommended that clinicians understand the technology's limitations and harness AI for administrative and documentation tasks rather than direct patient care. Future advancements could see AI helping with clinical support, provided there is transparency about data sources and rigorous validation processes to avoid the replication of biases and errors.<br /><br />Lastly, there was a call for involving people with lived experiences in developing these technologies to ensure they are beneficial and not harmful, as well as a need for policy recommendations to regulate AI's role in healthcare.

Keywords

AI in healthcare

large language models

ChatGPT

clinical settings

potential bias

privacy issues

mental health

misinformation

ethical approach

HIPAA compliance

clinical support

policy recommendations