Artificial Intelligence in Healthcare: A Review of Diagnostic Applications and Impact on Clinical Practice

Tufael^1*, Atikur Rahman Sunny²

This review discusses the AI’s integration into clinical medicine to improve diagnostic accuracy, improves patient care, and addresses challenges in healthcare decision-making.

Abstract

Background: Early detection of health issues is essential for effective treatment, necessitating advanced hospital service quality subsystems, ideally through an integrated Hospital Management Information System (HMIS). Artificial Intelligence (AI) has become a crucial element in clinical medicine, particularly in diagnostics. AI technologies, especially in medical imaging, are poised to revolutionize diagnostic and predictive analysis in healthcare. Methods: This study examines the application of AI in enhancing diagnostic accuracy and predictive capabilities in healthcare. It draws on evidence from pathology and dermatology studies, as well as research on AI's utility in mental health risk assessment and medical imaging. Results: AI has demonstrated superior performance compared to human experts in accurately detecting and classifying various cancers, such as those identified through pathology and dermatology studies. AI's capabilities extend beyond cancer detection to include the differentiation between benign and malignant conditions, thus aiding in more precise disease diagnosis. Additionally, AI shows promise in predicting mental health risks, including mental illnesses and suicide risks among vulnerable populations, such as psychiatric patients, prisoners, and soldiers. These applications highlight AI's potential to provide timely and precise disease information, leading to quicker and more accurate diagnoses. Conclusion: AI integration into healthcare significantly enhances diagnostic accuracy and optimizes disease management efficiency, improving patient outcomes and streamlining healthcare delivery. The technology's potential in medical imaging and mental health assessment underscores its value in providing early and accurate diagnoses, thereby supporting effective treatment and patient care. Continued research and development in AI applications are crucial to further realize its benefits in clinical settings.

Keywords: Artificial Intelligence (AI), Diagnostics, Clinical Decision Support Systems (CDSS), Medical Imaging, User Resistance

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