Does Explainability Enhance the Effectiveness of AI Models in Public Health? The COVID-19 Context

Neda Ahmadi, Mehrbakhsh Nilashi

Abstract


Generative AI models, such as ChatGPT, offer versatile applications in healthcare, particularly in the COVID-19 era. While these models show promise in medical decision support, the imperative of explainability cannot be overstated. Understanding how AI arrives at recommendations is crucial for transparency and trust, especially in critical areas like COVID-19 management. However, challenges persist in elucidating the decision-making processes of AI models, potentially hindering their acceptance in medical practice. This paper discusses the necessity of prioritizing explainability mechanisms tailored for AI-powered linguistic models, particularly in the context of COVID-19-related healthcare decisions. By shedding light on AI reasoning, explainability mechanisms not only enhance transparency and accountability but also foster trust among medical professionals, facilitating informed collaboration between human expertise and AI capabilities.


Keywords


Explainability, ChatGPT, COVID-19, XAI, Healthcare, Machine Learning

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Abstract

References


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