For various reasons, artificial intelligence (AI) in the medical sector is fast changing from a luxury to a need. Analyzing health data with conventional techniques becomes difficult given its rising bulk. More exact and faster medical decisions are made possible by AI's fast and accurate processing of enormous volumes of data. For radiology, for instance, artificial intelligence has been applied to raise diagnostic accuracy—especially in relation to cancer detection.

AI improves radiographical interpretation accuracy in diagnosis. AI has enhanced diagnosis rates in breast cancer by up to 30 percent by helping to spot trends or early indicators that human professionals might overlook and therefore help to lower unnecessary procedures. AI can ease strain on healthcare professionals by automating routine duties like medical image processing or scheduling, therefore freeing clinicians to concentrate more on direct patient care in view of the worldwide medical personnel shortage. Healthcare institutions in Saudi Arabia are incorporating AI chatbots and virtual assistants to handle administrative duties like appointment scheduling and answering basic questions.

AI also supports the creation of customized treatment regimens matched to individual patient data, including genetics and medical history, therefore producing better outcomes and less treatment side effects. In oncology especially, where tailored treatment plans might raise survival rates, this is especially important. For example, by streamlining cancer treatment paths with artificial intelligence, a U.S. healthcare provider saved $91 million yearly. Also, early interventions can be planned thanks to AI-powered predictive analytics that can predict disease risks or a patient's probability of deterioration. There has been a 20 percent decrease in patient readmissions in U.S. hospitals that have implemented AI-driven predictive analytics.