Artificial intelligence in disease prediction and diagnosis: a systematic literature review

Sun, 26 Oct 2025 00:00:00 -0400

Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in disease prediction and diagnosis, offering unprecedented opportunities to improve clinical outcomes and operational efficiency. This systematic literature review examines the current state of AI applications across various medical domains, focusing on its role in general disease diagnosis, cardiovascular diseases, cancer, and other specific conditions such as diabetes and Alzheimer's. We aim to synthesize existing research, identify key trends, and highlight gaps in the literature to guide future investigations. A rigorous methodology was employed to select and analyze relevant studies, ensuring a comprehensive evaluation of AI techniques, their performance, and clinical applicability. The findings reveal that AI models, particularly those based on deep learning and machine learning, demonstrate high accuracy in diagnosing diseases, often surpassing traditional methods. However, challenges such as data heterogeneity, interpretability, and integration into clinical workflows remain significant barriers. In cardiovascular diseases, AI excels in risk stratification and early detection, while in oncology; it enhances tumor classification and prognosis prediction. For chronic and neurodegenerative conditions, AI shows promise in personalized treatment planning. The review concludes that while AI holds immense potential, its widespread adoption requires addressing ethical, regulatory, and technical hurdles. Future research should prioritize robust validation, interdisciplinary collaboration, and real-world implementation to fully realize AI's benefits in healthcare.

Assessment of undiagonised anxiety and depression in patients attending teritiary care teaching hospital and role of clinical pharmacist in management

Uma Sankar Viriti, B.Kavya Sri, C.Aruna Kumari, P.Sindhuja, P.Sowmya — Sat, 20 Sep 2025 00:00:00 -0400

Background:This research focuses on observing the mental health conditions of patients who are suffering from undiagnosed anxiety and depression attending tertiary care teaching hospital. We have evaluated and observed the number of participants of the study are being suffered and assessed the role of clinical pharmacist in identification and management.Methodology: The present study is a prospective observational analytical study involving 135 study participants who are having anxiety & depression but are being undiagnosed at tertiary care hospital Anil Neerukonda Hospital in Vizianagaram district. The Information was obtained through direct interaction with patients from different departments and they’re diagnosed using State Trait Anxiety Scale (STAI) and Beck Depression Inventory Scale (BD-II).Results:In the study, we reported a prevalence of 51% suffering from anxiety, 32% are suffering from depression and 17% are suffering from both anxiety and depression. Among these 13% of the study participants are of Low/No Depression, 6% with Moderate depression and 2% with Severe Depression. Whereas participants with anxiety were reported as 9% suffering with Low/No Anxiety, 12% with Moderate Anxiety and 0.7% suffering from High Anxiety Levels.Conclusion: Patients who are suffering from undiagnosed anxiety and depression are found to be experiencing trauma, stress and other psychological issues which affects their mental health condition.

International Journal of Current Innovations in Advanced Research

Artificial intelligence in disease prediction and diagnosis: a systematic literature review

Assessment of undiagonised anxiety and depression in patients attending teritiary care teaching hospital and role of clinical pharmacist in management