Artificial intelligence (AI) has emerged as a transformative force in clinical diagnostics, demonstrating remarkable capacity to analyse complex medical data and enhance diagnostic precision across multiple specialties. This research article examines the integration of AI technologies including machine learning, deep learning, and convolutional neural networks in medical imaging, pathology, cardiology, and dermatology to improve early disease detection and diagnostic accuracy. Systematic review of recent literature reveals that AI models consistently achieve area under the curve (AUC) values exceeding 0.90, with sensitivity ranging from 91% to 96% for conditions such as breast cancer, pneumonia, and cardiovascular disease. Notably, AI systems have detected 19% of interval cancers in mammography screening that were initially missed by human radiologists, while deep learning algorithms for pneumonia detection from chest radiographs have demonstrated 96% sensitivity compared to 50% for traditional radiologist interpretation. However, significant challenges persist, including algorithmic bias, lack of explainability, data privacy concerns, and limited external validation. Ethical considerations surrounding health equity and the "black box" nature of complex models necessitate urgent attention. This article proposes a conceptual methodological framework for AI implementation, compares performance metrics across diagnostic domains, discusses implications for clinical workflow and patient outcomes, and provides actionable recommendations for regulatory standardization, interdisciplinary collaboration, and development of explainable AI systems to ensure responsible integration into modern healthcare.