Artificial intelligence in modern clinical practice (Review)

Abstract

Since its inception as rule-based programs, artificial intelligence (AI) has developed into machine learning and deep learning systems that utilize the enormous volumes of clinical data currently accessible. The aim of the present review was to discuss the role of AI in modern clinical practice and to highlight the opportunities and challenges that lie ahead by combining the results of recent research. AI tools provide physicians with decision support and prediction models, directs robotic procedures and surgical planning, supports radiologists, pathologists, dermatologists and ophthalmologists with image analysis, and aids in the delivery of more individualized care in cardiology and precision medicine. These developments are boosting precision, optimizing daily tasks and providing patients with more individualized treatment. In practice, this could include imaging systems that prioritize patients who are most at risk or prediction technologies that help physicians allocate resources and reduce unnecessary workload. However, there are still critical obstacles to overcome. The biases of the training data may be reflected in the algorithms, which could exacerbate already-existing disparities. Since many models operate as 'black boxes', it can be challenging to understand their logic, which raises questions about accountability, ethics and trust. Clinical standards and regulations are still lagging behind technology, and incorporating AI into busy healthcare systems can be difficult and costly. Achieving its promise will require careful implementation, rigorous validation and sustained collaboration among clinicians, data scientists, engineers, ethicists and policymakers for safe adoption in clinical practice.

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