The Importance and Applications of Artificial Intelligence in Healthcare

Ammon

19-07-2025

There is a significant transformation occurring in healthcare, driven by artificial intelligence (AI), which is expected to enhance healthcare management efficiency, make care more patient-centered, and improve clinical services. In Jordan, we are not far removed from these developments. However, I am critical of the current direction, which tends to marginalize clinical physicians—the true bridge between healthcare and patients. When a patient visits a hospital, they are not primarily concerned with healthcare administration or supporting medical and non-medical staff, important as they may be. Rather, the patient seeks to see and consult with a clinical specialist.
AI utilizes complex algorithms to analyze vast amounts of input data to find patterns that support healthcare services, such as automation, reducing human error, predicting outcomes, and contributing to the development of remote virtual clinics. In my opinion, the influence of AI is still largely confined to administrative procedures, policies, and health system management, rather than being fully integrated into practical clinical care. This raises the question of what aspects of AI we should adopt in clinical practice and what we should leave aside.
I do not foresee AI replacing doctors in the near future. Instead, it will enhance certain diagnostic processes, such as radiological and laboratory test interpretation. However, clinical assessment— the cornerstone of medical practice—will remain reliant on human physicians and their acquired knowledge and clinical skills. Let me illustrate this point: when MRI technology first emerged, many expected it to significantly reduce the need for diagnostic physicians. However, we later learned that human interpretation is crucial. For instance, if we perform spinal MRIs on people without any back pain, we find that 60% show disc changes. In many of these cases, the changes are significant and similar to those in patients with severe pain. Thus, a clinical assessment is essential to correlate imaging findings with the patient's symptoms. Moreover, not all large disc herniations are clinically significant. As a clinical physician, I may choose to treat a smaller disc herniation if it is the true cause of symptoms, and leave the larger, asymptomatic ones untreated—for the sake of better patient outcomes.
This example, which can be extended to AI, demonstrates that while data analysis and prediction can assist the physician, clinical evaluation and decision-making remain human responsibilities.
As for AI chatbots in virtual health, the social factor must not be overlooked. Our clinical experience tells us that patients are not satisfied speaking only to nurses or physician assistants—they feel reassured only after meeting the specialist. Will patients in our region accept or trust medical advice from a chatbot? In my experience, patients are influenced by digital information (like that from AI systems), but do not trust it fully nor consider it a substitute for direct consultation with a qualified physician. Future generations may differ, but that remains to be seen.
AI may prove useful in remote monitoring—such as in epilepsy diagnostics, cardiac monitoring, or sleep studies. These applications are crucial if implemented properly, as they can reduce hospital admissions, conserve resources, and provide better data on the patient's condition by observing them in their natural home environment.
Finally, the psychological factor—not mental illness per se, but emotional well-being—must be considered. Can AI assist in this area? If so, it would be a significant advancement, as stress, anxiety, and psychological pressure play major roles in the onset and progression of many symptoms, and they can influence treatment outcomes. One relevant example is pain—an important aspect of our clinical work. Pain is a subjective experience, difficult to measure, and varies widely among individuals. Some people are more sensitive and have a lower pain tolerance, which may lead to dissatisfaction with treatment and healthcare services.