AI in Healthcare: Augmenting Care, Preserving Humanity - Florez, Hector; Puttegowda, Kiran (Revista Científica, 2026)
Medicine is based on human expertise, experiential learning, and ethical judgment. However, in recent years, it has been supported by algorithms capable of analyzing large amounts of data with high speed and precision. While these technologies offer efficiency, accuracy, and accessibility, they also raise questions about trust, equity, and the role of healthcare professionals. Artificial intelligence (AI) systems—specifically systems that operate on machine and deep learning platforms—are good at recognizing patterns in large datasets, but they do not possess inherent and actionable knowledge. This contrast brings a root cause of tension into the limelight: the need to incorporate algorithmic intelligence into the sphere, where human judgment cannot and will never be dispensed with. The new achievements of medical AI have been impressive. Deep networks are currently competitive or even superior to human professionals in applications like tumor detection in radiological images, the detection of diabetic retinopathy in retina scans, and the detection of cardiac abnormalities in electrocardiograms. Large language models (LLMs) can be helpful to clinicians by summarizing patient records, writing discharge notes, and offering evidence-based suggestions. These developments have resulted in an optimistic view that diagnostic errors can be reduced, the workload on clinicians can be minimized, and proper healthcare can be provided to underserved areas.