ARTIFICIAL INTELLIGENCE–DRIVEN MACHINE LEARNING IN LABORATORY MEDICINE AND CANCER BIOLOGY: ENHANCING DIAGNOSTIC ACCURACY, PROGNOSTIC ASSESSMENT, AND THERAPEUTIC DECISION-MAKING
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Objective: Artificial intelligence (AI) and machine learning (ML) are progressively revolutionizing laboratory medicine and cancer biology by augmenting diagnostic precision, refining prognostic evaluation, and facilitating individualized therapeutic decision-making. The increasing complexity of clinical data, along with ongoing diagnostic errors and variations in treatment outcomes, has made a strong case for integrating AI-driven analytical methods into modern healthcare systems. Method: This discussion examines how AI's role is changing across the diagnostic, prognostic, and therapeutic continua. It discusses both its clinical impact and the challenges of implementing it. Results: Diagnostic tools powered by AI are very effective at interpreting lab data, medical images, and histopathology. They are often just as accurate and consistent as traditional methods. In oncology, AI-driven prognostic models amalgamate multidimensional datasets, encompassing clinical, imaging, genomic, and proteomic data, to yield more accurate forecasts of disease progression, recurrence, and survival. These features directly support precision medicine by enabling patients to be grouped by risk and treated on an individual basis. AI-powered clinical decision support systems also help doctors choose the best treatment options by combining extensive evidence, real-world outcomes, and patient-specific traits. Novelty: Many AI models are "black boxes," making it hard to understand how they work and reducing doctors' trust in them. Also, differences in infrastructure and resources make it harder to use AI fairly, especially in low- and middle-income countries. To fully realize the potential of AI, it will be essential to deal with ethical, technical, and infrastructure issues. This will lead to better, more efficient, and more patient-centered healthcare.
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