ARTIFICIAL INTELLIGENCE IN RADIOLOGY: CURRENT APPLICATIONS AND FUTURE DIRECTIONS
TL;DR
This review examines the integration of AI across domains, including robotic surgery, drug development, medical imaging, epidemiology, and clinical decision-making, to guarantee sustainable AI deployment and foster global cooperation to make AI-driven medical solutions inclusive and accessible.
Abstract
In the healthcare industry, artificial intelligence (AI) has significantly enhanced treatment plans, pharmaceutical advancements, hospital administration, and diagnostic precision. This review examines the integration of AI across domains, including robotic surgery, drug development, medical imaging, epidemiology, and clinical decision-making. Techniques like deep learning and natural language processing (NLP) have proven remarkably effective in the domains of medical image interpretation, illness trajectory prediction, and healthcare infrastructure optimisation. However, increasing the fairness and transparency of AI is crucial to gaining the trust of patients and medical professionals. Looking forward, future advancements in medical AI are anticipated to be primarily driven by generative AI, federated learning, and multimodal AI. Instead of taking the place of human knowledge, artificial intelligence (AI) will be used as a supplementary tool to help healthcare professionals make better clinical decisions by giving them data-driven insights. It will be crucial to guarantee sustainable AI deployment and foster global cooperation to make AI-driven medical solutions inclusive and accessible. The success of AI in imaging will be measured by worth creation, which includes better patient outcomes, faster turnaround, higher diagnostic certainty, and a higher quality of work life for radiologists. AI offers a novel and fascinating collection of methods for analysing image data. Radiologists will likely be at the forefront of AI's medical applications as they explore these new possibilities.