AI Accuracy in Health Care: From Diagnosing to Disease Prediction, Challenges and Opportunities.

Abstract

The research explores the transformative impact of Artificial Intelligence (AI) on healthcare, focusing on its advancements in diagnostics and disease prediction. It traces the evolution of machine learning algorithms from rule‑based systems to sophisticated deep learning techniques, such as CNN for medical imaging and NLP for EHRs. Recently, AI has a critical role in disease identification, citing its application in predicting sepsis, cancer prognosis, and cardiovascular conditions, while also addressing challenges related to data bias and generalizability in diagnostic accuracy. This research examines the utility of AI, discussing the dichotomy between explainable AI (XAI) and black‑box systems, emphasizing the necessity of transparency and trust in clinical settings. Furthermore, the research investigates how conversational AI can enhance patient‑doctor communication and facilitate shared decision‑making, exploring triadic collaboration models involving clinicians and AI systems. Looking ahead, the continued advancement of predictive AI is expected to reshape healthcare by enabling more personalized, proactive, and data‑driven medical decision‑making. In addition, predictive AI has the potential to reduce healthcare inequalities by expanding access to high‑quality diagnostic and treatment services in underserved communities. Although AI has already demonstrated significant improvements in the accuracy and efficiency of healthcare delivery, further research and collaborative efforts are required to enhance model interpretability, ensure ethical implementation, and develop scalable solutions that maximize its impact across diverse healthcare systems worldwide.