Redefining Differential Diagnosis in Chronic Diseases: The Emerging Role of Artificial Intelligence
DOI:
https://doi.org/10.5281/zenodo.18213005Keywords:
Artificial intelligence, treatment, chronic diseases, predicting stepsAbstract
Aim
Overlapping symptoms, involvement of several systems, and lengthy illness trajectories make differential diagnosis of chronic diseases extremely challenging in clinical practice. Disease progression, higher healthcare costs, and worse quality of life are consequences of incorrect or delayed diagnosis. Artificial intelligence (AI) has recently opened up new possibilities for improving clinical decision-making and diagnostic accuracy in complex chronic diseases.
Focusing on their capacity to integrate multidimensional clinical data and support medical decision-making, this study aims to assess the performance of AI-based methods in improving the differential diagnosis of chronic diseases.
Material and Methods
A literature search was conducted in PubMed and Medline from 2000 to 2025. A narrative review of artificial intelligence (AI) applications on chronic diseases was conducted.
Results
Artificial intelligence (AI) diagnostic models outperformed human clinicians in distinguishing among inflammatory, autoimmune, metabolic, and neurological diseases that share similar symptoms. Compared with traditional clinical evaluations, these systems performed much better at identifying subtle disease-specific traits and recognizing complex patterns across large datasets. There was hope that AI-assisted tools could help shorten diagnostic wait times and bolster individualized treatment plans.
Conclusion
By improving pattern identification, data integration, and diagnostic accuracy, artificial intelligence shows promise as an auxiliary in the differential diagnosis of chronic diseases. Although AI systems can't replace human doctors, they could help diagnose patients faster and more accurately if used routinely. To ensure safe and effective adoption in chronic disease management, future research should focus on model transparency, clinical validation, and ethical implementation.
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