Advancing Clinical Decision-Making using Artificial Intelligence and Machine Learning for Accurate Disease Diagnosis

The paper discusses on how the fusion of AI and ML techniques can lead to more precise disease detections, ultimately revolutionizing clinical decision-making at the diagnostic level. The evolution of computational algorithms and outbursts of healthcare data catalyzed the development of AI and ML models that can improve diagnostic precision. Such models harness large volumes of data (e.g., electronic health records, imaging, genomic data) to discover complex relationships/associations that are likely difficult for human clinicians to recognize. AI systems improve diagnostic accuracy, reduce errors and incorporate predictive modeling of disease prognostics and treatment outcomes using supervised and unsupervised machine learning techniques. It relies heavily on deep learning, natural language processing techniques, and neural networks to process and extract meaning from varying data modalities. The artificial intelligence (AI)-driven decision-support systems helps physicians navigate the growing quantities of data, leading to an improved distribution of workflow and proper distribution of resources. While their potential is already displayed, there remain key barriers: model interpretability, implementation into clinical workflows and data privacy.