Patient Outcomes

ABSTRACT Background: Artificial Intelligence (AI) has rapidly integrated into healthcare, proving indispensable in diagnostic processes. Event-predicting equations in medicine offer solutions to longstanding issues related to early diagnosis and personalized patient care. Objective: This article aims to explore best practices in objective and quantitative diagnostic predictions using AI and predictive algorithms. It seeks to revolutionize healthcare diagnostics by enhancing effectiveness and reducing diagnostic error rates. Methods: This study involves a literature review of the past five years, focusing on recent innovations in AI for healthcare diagnostics. The review includes fields such as oncology, cardiology, and others to evaluate the efficacy of prediction algorithms in practice. Results: The findings indicate that machine learning-based computer-aided diagnosis models significantly improve diagnostic accuracy by detecting diseases at early stages and personalizing treatment programs. The integration of these algorithms has led to reduced diagnostic errors and improved patient experiences across various medical fields. Conclusion: AI predictive algorithms represent the future of diagnostic medicine. Their adoption is set to personalize and advance patient treatment, enhance health outcomes, and improve the efficiency of healthcare systems. However, comprehensive research and precise implementation are essential to fully harness the potential of AI in diagnostics.