Health science reports · 2026
Evaluation of the Diagnostic Capabilities of Artificial Intelligence (GPT-4) in a Cardiology Department in Sub-Saharan Africa: Cross-Sectional Study.
СтатьяНазвание и аннотация: Перевод в очереди
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Nacanabo Martin Wendlassida, Seghda André Arthur Taryètba, Tall/Thiam Anna, Millogo Georges Christian R, Bayala Yannick Laurent Tchenadoyo, Yameogo Valentin Nobila, Samadoulougou André Koudnoaga, Zabsonre Patrice
Аннотация
CONTEXT/OBJECTIVE: "Generative Pre-trained Transformer" type 4 or GPT-4 is an artificial intelligence (AI) developed by OpenAI. It can be used to generate positive and differential diagnoses. However, its effectiveness in a cardiology department in Africa has not been studied. The aim of this study is to evaluate the diagnostic accuracy of GPT-4 in the cardiology department.
METHODS: This was a retrospective descriptive cross-sectional study conducted from April 1, 2024 to May 30, 2024 in the cardiology department of the Bogodogo University Hospital. Our primary endpoint was whether the main diagnosis of GPT-4 corresponded to the final diagnosis made by the cardiologists.
RESULTS: Out of 50 patients collected, GPT-4 found the diagnosis on the basis of clinical data in 35% (18/50). In 82% (41/50) of cases, GPT-4's diagnosis was one of the cardiologist's three hypotheses and in 64% (32/50) of cases the diagnosis was found with certainty by GPT-4. The differential diagnosis listed by GPT-4 was scored 5 in 46 patients. All the diagnoses of the aetiological groups were found by GPT-4 in 100% (50/50) of cases, except in the hypertensive and ischaemic cardiomyopathy groups.
CONCLUSION: GPT-4 demonstrated a variable ability to generate accurate diagnoses, with a significant improvement when paraclinical data were included.
METHODS: This was a retrospective descriptive cross-sectional study conducted from April 1, 2024 to May 30, 2024 in the cardiology department of the Bogodogo University Hospital. Our primary endpoint was whether the main diagnosis of GPT-4 corresponded to the final diagnosis made by the cardiologists.
RESULTS: Out of 50 patients collected, GPT-4 found the diagnosis on the basis of clinical data in 35% (18/50). In 82% (41/50) of cases, GPT-4's diagnosis was one of the cardiologist's three hypotheses and in 64% (32/50) of cases the diagnosis was found with certainty by GPT-4. The differential diagnosis listed by GPT-4 was scored 5 in 46 patients. All the diagnoses of the aetiological groups were found by GPT-4 in 100% (50/50) of cases, except in the hypertensive and ischaemic cardiomyopathy groups.
CONCLUSION: GPT-4 demonstrated a variable ability to generate accurate diagnoses, with a significant improvement when paraclinical data were included.