The Role of Artificial Intelligence in Diagnosing Drug-Induced Hepatitis: A Systematic Review on Differentiation from Viral Hepatitis

DILI presents symptoms like those in viral hepatitis in terms of elevated liver enzymes, jaundice, and liver dysfunction. These make it rather difficult to differentiate between DILI and viral hepatitis through traditional diagnostic methods. DILI is of profound relevance to global morbidity and mortality today. Thus, accurate diagnosis in terms of time and precision is essential. AI offers hope and promises to use improved diagnostics for personalized treatment strategies. After a systematic search on PubMed and Google Scholar, 933 studies were identified, and they concentrated on AI applications in differentiation regarding DILI and viral hepatitis. Only 55 studies were shortlisted for evaluation via a review process that covered diverse AI techniques deployed in diagnosis, including performance metrics. Models, AI have improved how DILI is diagnosed into a paradigm of distinctive biomarkers in a cross-section of clinical data. Machine learning algorithms using clinical data and imaging have very high accuracy in distinguishing DILI from viral hepatitis. It will improve early diagnosis, prognostic predictions, and novel therapeutic target identification by AI with multimodal-data cross-images, laboratory tests, and clinical history. Indeed, AI has the potential to be instrumental in enhancing the diagnosis of DILI and its differentiation from viral hepatitis. With the development of further advanced models, AI could even act as predictive compared to giving the facilities for drug-induced hepatotoxicity and thus enhance patient outcomes while reducing the cost-benefits for richer pharmaceutical processes.