Medical tele-diagnoses in countries with limited resources: Comparison of a general generative AI system with a clinical decision support system

Abstract

<title>Abstract</title> <bold>Introduction</bold>: Achieving correct clinical or morphological diagnoses in countries with limited resources is a major challenge due to the lack of methods such as immunohistochemistry, molecular biology or imaging, as well as the lack of specialists. Artificial intelligence (AI), either in the form of generative intelligence or in the form of clinical decision support systems (CDSS), is a promising method for bridging the gap between diagnosis in developed countries and countries with limited resources. For this purpose, we used the general generative AI system ChatGPT and the specialised semantic net-based AI system Memem7 as medical diagnostic support systems to improve telemedicine diagnosis in a resource-limited country. <bold>Materials and methods</bold>: 102 randomly selected cases from 3 hospitals in northern Afghanistan were classified by up to 7 telemedicine experts. In 61 cases (59.8%), the experts provided a disease classification (target diagnosis). In the remaining 41 cases, the experts only provided a list of differential diagnoses. We investigated how often ChatGPT and Memem7 were able to predict the target diagnosis or provide a list of essential differential diagnoses (DD). <bold>Results</bold>: In 36/61 (59.0%) and 47/61 (77.1%) cases, respectively, ChatGPT and Memem7 recognised the target diagnosis. In 88/102 (86.3%) (ChatGPT) and 93/102 (91.2%) (Memem7) cases, a helpful list of differential diagnoses was provided. <bold>Conclusions</bold>: Both AI-based systems show promising results, either in confirming the target diagnosis or in providing a helpful list of differential diagnoses.

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