Artificial Intelligence in Clinical Decision Support

Abstract

Artificial Intelligence (AI) is revolutionizing the healthcare industry by improving medical research, diagnosis, therapy, and patient care. AI systems use machine learning, natural language processing, and computer vision to analyze vast volumes of medical data, assist physicians in making informed decisions, and develop individualized treatment regimens. AI is also crucial for early illness detection, health outcome prediction, and process improvement in the medical field. However, to fully leverage AI in healthcare, several problems must be resolved, including patient data protection, ethical considerations, and legal constraints. AI has the potential to revolutionize healthcare by assisting physicians in making more informed decisions, enhancing patient safety, and mitigating the impact of staffing shortages. Regulators and politicians are concerned about the reliability of Clinical Decision Support Systems (CDSSs) and Artificial Intelligence (AI), as well as whether users trust them. This study examines these disparities with a particular focus on physicians' perceptions of AI and trust in CDSS. Clinical Decision Support Systems (CDSS) are currently successfully using AI, which can be classified into two categories: data-driven and knowledge-based. AI is particularly helpful for tasks like determining the reasons for cardiac enlargement, evaluating ECG data, and detecting electrolyte imbalances. However, AI has drawbacks, such as a lack of accountability for incorrect medical judgments or treatment outcomes. An ageing population is predicted to make the skilled labor shortage in the healthcare industry worse, and AI-based Clinical Decision Support Systems (AICDSS) have shown benefits in reducing workload and enhancing healthcare. AICDSS has gained recognition for its ability to use both organized and unorganized clinical data to help patients and healthcare professionals in various circumstances. However, AI-based AICDSS must be more effectively incorporated into existing healthcare practices to use vast amounts of data while adhering to stringent privacy regulations and ensuring user satisfaction. To promote the application of AICDSS in clinical settings and achieve wider acceptance, a comprehensive understanding of the variables influencing physicians' inclination to use AI-CDSS and their relationships with each other is needed.

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