ChatGPT and health care: implications for interoperability and fairness

ChatGPT and health care: implications for interoperability and fairness

ChatGPT has sparked an immense interest within the health care community. It is an example of large language models that use deep learning algorithms to process natural language and generate responses to user inputs in a conversational manner. This technology has the potential to improve the way patients interact with health care providers and enhance the overall quality of health care services.   Health care providers would have a vested interest in utilizing this technology to their advantage, leveraging their own medical data to train their own model and improve operational efficiency within their own organization. This could lead to a fragmented landscape with multiple AI programs, each trained on a proprietary fraction of the overall data available and thus of lesser quality and lesser diagnostic acuity than would have been possible had all the data been available.

Medigy Insights

The introduction of ChatGPT and similar large language models has generated significant interest in the healthcare community. These models, utilizing deep learning algorithms, offer the potential to improve patient interactions and enhance healthcare quality. Healthcare providers can leverage ChatGPT to optimize operational efficiency by training customized models with their own medical data. However, this approach may lead to data fragmentation, with multiple AI programs trained on proprietary subsets of data, resulting in lower diagnostic accuracy. Collaboration, standardization, and ethical considerations are crucial to address these challenges and maximize the benefits of AI in healthcare while preserving data integrity and quality.


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