Advancing Healthcare Through Machine Learning: Opportunities, Challenges, and Solutions for Integration

Abstract

This comprehensive review examines the adoption of Machine Learning (ML) in healthcare, exploring its opportunities, obstacles, and potential solutions. The primary aim is to thoroughly investigate ML's integration into medical practices, showcase its effects, and offer pertinent remedies. The study is driven by the need to understand the complex implications of ML's convergence with healthcare services. Through careful examination of current research, this approach illuminates the wide range of ML applications in disease forecasting and tailored treatment. The study's accuracy is rooted in its detailed analysis of methodologies, scrutiny of research, and extraction of crucial insights. The paper confirms ML's success in various medical care domains. ML algorithms, especially Convolutional Neural Networks (CNNs), have shown high precision in detecting diseases like lung cancer, colorectal cancer, brain tumors, and breast tumors. Besides CNNs, other algorithms including SVM, RF, k-NN, and DT have also shown effectiveness. Assessments based on accuracy and F1-score reveal satisfactory outcomes, with some studies surpassing 90% accuracy. This key finding emphasizes the remarkable precision of ML algorithms in diagnosing various medical conditions. This result indicates ML's potential to revolutionize traditional diagnostic methods. The discussion addresses challenges including data quality issues, security concerns, possible misinterpretations, and hurdles in implementing ML in clinical settings. To address these issues, multifaceted solutions are suggested, including standardized data formats, robust encryption, model interpretation, clinician education, and collaboration among stakeholders.