Authors: David Mark Abayomi, Obafaiye Pauline Olayemi
Abstract: The integration of Artificial Intelligence (AI) into healthcare is revolutionizing the paradigms of diagnosis and treatment (Topol, 2019). This paper provides a comprehensive review of contemporary AI applications, focusing on machine learning (ML) and deep learning (DL) models in image analysis, predictive analytics, and precision medicine. We conducted a systematic literature review of peer-reviewed articles and major clinical trials published between 2018 and 2023. Our analysis demonstrates that AI algorithms, particularly con- volutional neural networks (CNNs), now match or exceed human expert performance in diagnosing specific conditions from radiological (e.g., mammography, chest X-rays) and pathological images (Liu et al., 2021). In treatment, AI-driven tools are enhancing radiotherapy planning, predicting patient-specific drug responses, and powering clinical decision support systems (He et al., 2019). The discussion highlights transformative case studies, including AI for early sepsis detection and diabetic retinopathy screening, while critically addressing significant challenges: algorithmic bias (Obermeyer et al., 2019), the ”black box” problem, data privacy concerns, and the necessity for robust clinical vali- dation and regulatory frameworks (FDA, 2021). We conclude that AI holds immense potential to augment clinical decision-making, improve diagnostic accuracy, personalize treatment, and alleviate administrative burdens. However, its successful translation into routine care necessitates a collaborative focus on ethical AI development, interdisciplinary education, and human-centered design to ensure these tools are equitable, transparent, and effectively integrated into the clinical workflow.
DOI: https://doi.org/10.5281/zenodo.18372826
