The use and integration of AI into medicine leads to a quiet revolution in health care
By Professor Nicos Savva Professor of Management Sciences at the London Business School
Speaking to the recent SXSW London Festival, former British Prime Minister Sir Tony Blair said the United Kingdom should adopt a future of AI doctors and nurses, or risk being left in the biggest upheaval since the industrial revolution. He continued by saying that fears of artificial intelligence should be counterbalanced by the “absolutely transformative” impact that it could have on public services such as health care and education by saving time and money – “When I retreat and look at what AI does, I think that we are in the buttresses of the most transformative revolution since the 19th century industrial revolution”.
This daring affirmation of the former Prime Minister follows Hot on the news of News out of Saudi Arabia, concerning the first world Doctor clinic powered by AIwhere the “Dr. Hua” by Synyi Ai, based in Shanghai, diagnostic and already prescribes the treatment of respiratory diseases. He paints a living image of a future where artificial intelligence takes care of our health independently in a way that was considered to be science fiction only a few years ago. But while such developments arouse reflection, I firmly believe that the true and punchy role of the short and medium term AI is not to replace human clinicians, but to increase them powerfully.
There are imperative reasons for this perspective, extending beyond the obvious concern of the potential error and the serious consequences should occur in a medical context. The complex health care ecosystem itself is just as important. Clinicians are not just service providers; They are key stakeholders deeply invested in their roles, especially in the most critical and intensive aspects of patient care. They will legitimately quote security problems during the contemplation of the famous this control, but it would be naive to ignore financial incentives and professional autonomy which also shape their approach.
Consequently, the integration of AI into health care rightly started in auxiliary roles. We have already seen the Excel IA in tasks such as maintaining notes, writing letters and documents management – administrative charges which often harm the direct interaction of patients. This development will regularly progress towards passive surveillance, for example, the verification of prescription drug dosages, the identification of potential interactions and contraindications against contraindications. From there, AI will pass to more active consultative capacities, suggesting diagnoses, recommending follow -up surveys and describing possible treatment options. However, the final and nuanced decision will remain firmly in the hands of a highly qualified human expert.
This arc measured from the integration of AI presents a deep “win-win” scenario. For doctors, it offers a way for considerably increased work satisfaction and a reduced scholarship. I have not yet met a doctor who really appreciates endless documents; Many would gladly give up part of their salary to alleviate this burden. By unloading these AI tasks, doctors can recover precious time, focusing their energy on what really matters: direct commitment of patients and complex problem solving.
Patients are also enormously benefited. Imagine a consultation where your doctor establishes a real visual contact, actively listens and engages in a conversation, rather than type or constantly watching their screen. This improved human connection, facilitated by AI, the management of the basic administrative load, promises a more empathetic and efficient health experience. For health systems, the advantages are also convincing.
Refund often depends on the quality and precision of medical notes, an area where AI can make substantial improvements. By improving the accuracy and completeness of documentation, AI can rationalize processes and strengthen financial stability.
The Quiet Revolution of AI in health care
Slowly but surely, however, the conversation will increase from an increase to the replacement. This transition is likely to occur first in less critical areas, such as routine monitoring visits or standard controls. More importantly, it can be held in resources related to resources where the alternative to AI is not a highly qualified human professional, but rather nothing at all. Some industry observers fear that this will lead to a two -level health system, where human doctors take care of the rich, while the least privileged are found with a lower substitute and focused on AI for the care they really need. This, according to them, will exacerbate existing health inequalities.
Personally, I believe they are right to worry about exacerbating existing health inequalities, but I think they could have history upside down. While the AI continues its incessant improvement, it is plausible that at one point, perhaps earlier than many do not plan – it will exceed human doctors in all dimensions, including delicate art in the way of bedside and empathy. When this happens, perhaps the opposite scenario will take place: the wealthy world will be treated by Dr. Superior Dr AI, while the least privileged can be found to access these expensive costly AI systems and protected by patents, and must rather face the relatively lower human alternative. It is a provocative thought, but we must seriously consider as we sail in the extraordinary potential and the deep ethical implications of AI in health care.
Professor Nicos Savva is professor of management science at London business school And an expert in data science, using them to solve operational problems and help major organizations develop data science capacities. His research at LBS focuses on the management of health care, including hospital operations, the regional organization of care, performance evaluation, measurement of health inequalities and innovation. Professor Savva’s work appeared in leading journals such as management science, operations management and service services and nature biotechnology.
