{"id":3227,"date":"2026-09-15T06:25:17","date_gmt":"2026-09-15T06:25:17","guid":{"rendered":"https:\/\/www.malereproduction.com\/blog\/?p=3227"},"modified":"2026-09-15T06:25:17","modified_gmt":"2026-09-15T06:25:17","slug":"artificial-intelligence-in-medicine-clinical-decision-making-and-why-the-physician-must-remain-in-the-loop","status":"publish","type":"post","link":"https:\/\/www.malereproduction.com\/blog\/artificial-intelligence-in-medicine-clinical-decision-making-and-why-the-physician-must-remain-in-the-loop\/","title":{"rendered":"Artificial Intelligence in Medicine, Clinical Decision-Making, and Why the Physician Must Remain in the Loop"},"content":{"rendered":"\n

By Philip Werthman, MD, MMH<\/a><\/em><\/p>\n\n\n\n

Artificial Intelligence Is Already Changing Medicine<\/h2>\n\n\n\n

Artificial intelligence is no longer a futuristic concept in healthcare. It is already being used in hospitals, radiology departments, pathology laboratories, medical offices and research centers around the world. As a physician and surgeon, I wanted to understand not only what AI can do, but where its limitations and dangers lie. My studies at Harvard Medical School focused on the rapidly evolving role of artificial intelligence in healthcare, and I came away with two strong conclusions: the opportunity is enormous, and the need for caution is equally enormous.<\/p>\n\n\n\n

AI has the potential to make medicine more accurate, more efficient and more personalized. It can analyze amounts of information that no human being could possibly review in a lifetime. But healthcare is different from most industries. A mistake in medicine can injure or even kill a patient. For that reason, the question should not be whether AI will replace doctors. The better question is how physicians can use AI to make better decisions while preserving human judgment and accountability.<\/p>\n\n\n\n

From Early Artificial Intelligence to Large Language Models<\/h2>\n\n\n\n

The history of artificial intelligence stretches back decades, but the technology accelerated dramatically when three developments converged: enormous computing power, massive digital datasets and increasingly sophisticated machine-learning algorithms.<\/p>\n\n\n\n

A major turning point came with the development of transformer architecture in 2017. Transformers allowed computers to analyze relationships between words and concepts far more effectively than earlier systems. That breakthrough helped lead to the modern large language model, or LLM.<\/p>\n\n\n\n

LLMs are trained on enormous quantities of text and data. They do not simply retrieve a stored answer. They recognize patterns and predict relationships between words, ideas and concepts. Today, advanced AI systems can summarize medical records, compare laboratory trends, analyze medical literature, draft clinical documentation and help organize complicated diagnostic information.<\/p>\n\n\n\n

Medicine is an ideal environment for this technology because modern healthcare produces an overwhelming amount of data: laboratory tests, imaging studies, pathology slides, genetic information, medications, medical records and millions of scientific publications. No individual physician can process all of it. Artificial intelligence potentially can.<\/p>\n\n\n\n

Why Radiology, Pathology and Dermatology Are Natural Uses for AI<\/h2>\n\n\n\n

Some medical specialties are particularly well suited to AI because so much of the work involves visual pattern recognition. Radiology is perhaps the clearest example. A radiologist may review thousands of X-rays, CT scans and MRIs looking for abnormalities that can be extremely subtle. Artificial intelligence can be trained on enormous image datasets and can sometimes identify patterns that a human eye may not readily appreciate.<\/p>\n\n\n\n

The same principle applies to pathology. Digitized pathology slides contain vast quantities of visual information. An AI system can systematically analyze those images and help flag areas that may represent malignancy or another abnormal process.<\/p>\n\n\n\n

Dermatology is another natural application. Skin disease is often diagnosed by pattern recognition. AI can compare an image of a lesion against an enormous database and help a dermatologist decide whether something appears benign, suspicious or worthy of biopsy.<\/p>\n\n\n\n

This does not mean AI should replace the radiologist, pathologist or dermatologist. In my view, the strongest model is physician plus artificial intelligence. AI becomes another set of eyes, another analytical tool and another layer of quality control. It does not become the doctor.<\/p>\n\n\n\n

The Major Risk: AI Can Hallucinate<\/h2>\n\n\n\n

One of the most important limitations of artificial intelligence is that it can be extremely convincing when it is wrong. Large language models can hallucinate. They can generate an answer that sounds logical, polished and authoritative while containing information that is inaccurate, fabricated or unsupported.<\/p>\n\n\n\n

That is inconvenient in ordinary life. In medicine it can be dangerous.<\/p>\n\n\n\n

An AI system could misinterpret a laboratory abnormality, invent a drug interaction, miss an important finding on an image or identify a disease process that is not actually present. Image-based systems can also produce false positives and false negatives. In other words, AI may see something that is not there, or fail to see something that is.<\/p>\n\n\n\n

Human physicians make errors as well, and one of the great promises of AI is that properly validated systems may help us reduce those errors. But AI introduces a new category of risk: automation bias. The more sophisticated and confident a computer-generated answer appears, the easier it becomes for a clinician to accept it without challenging it. That is precisely why human oversight remains essential.<\/p>\n\n\n\n

FDA Oversight of Artificial Intelligence in Healthcare<\/h2>\n\n\n\n

The growth of AI-enabled medical technology has been remarkable. The U.S. Food and Drug Administration maintains a list of AI-enabled medical devices that have met applicable premarket requirements, and that list expanded past 1,000 authorized devices by the end of 2024 and has continued to grow. Radiology represents a particularly large share of these technologies.<\/p>\n\n\n\n

The regulatory question becomes more difficult as AI moves from assisting clinicians to generating diagnostic, prognostic or treatment recommendations. The FDA itself has recognized that newer forms of AI, including generative AI and large language models, create new regulatory challenges. The greater the clinical risk and the more independently an algorithm operates, the greater the need for validation, transparency, monitoring and human oversight.<\/p>\n\n\n\n

I would be very cautious about any system that attempts to remove the physician or qualified healthcare professional from a consequential clinical decision. There is an enormous difference between a computer helping a doctor recognize an abnormality and a computer independently deciding what should happen to a patient.<\/p>\n\n\n\n

How AI Can Improve Healthcare Without Replacing Doctors<\/h2>\n\n\n\n

Some of the most valuable uses of AI may initially be less dramatic than autonomous diagnosis. AI can summarize years of medical records before a consultation. It can organize laboratory trends, identify potentially important changes, search relevant medical literature, assist with documentation and reduce repetitive administrative work.<\/p>\n\n\n\n

Imagine seeing a patient with a complicated ten-year history and having the relevant diagnoses, medications, imaging results, laboratory trends and previous treatments organized before the patient even walks into the room. Imagine being able to rapidly review the most relevant published studies while considering a difficult treatment decision. Imagine eliminating a large portion of the clerical burden that keeps physicians staring at computer screens instead of talking to their patients.<\/p>\n\n\n\n

Those applications could make medicine substantially better. They could give physicians more information, reduce inefficiency and allow us to spend more time doing what technology cannot easily replace: listening, examining, interpreting context and establishing trust.<\/p>\n\n\n\n

The Most Important Lesson I Took From Harvard Medical School<\/h2>\n\n\n\n

What I learned at Harvard Medical School made me more enthusiastic about artificial intelligence, not less. But it also made me more aware of the responsibility that comes with it.<\/p>\n\n\n\n

AI can analyze more information than I can. It can compare millions of images, search thousands of scientific papers and detect patterns that I may not immediately recognize. Those capabilities will continue to improve.<\/p>\n\n\n\n

But medicine is not simply pattern recognition. A patient has a history, family, fears, preferences, competing medical conditions and circumstances that can completely change the correct decision. Medicine also requires responsibility. Ultimately, someone must be accountable for the recommendation that is made.<\/p>\n\n\n\n

That is why I believe the future of AI in healthcare should be built around augmentation rather than replacement. Artificial intelligence should help physicians see more, know more and work more efficiently. It should provide another analytical layer and reduce avoidable mistakes. But it should not be allowed to independently replace clinical judgment in decisions that can materially affect a patient’s health.<\/p>\n\n\n\n

Used responsibly, AI may become one of the most important advances in the history of medicine. Used without appropriate safeguards, it could create an entirely new set of medical risks.<\/p>\n\n\n\n

The technology will continue to advance at extraordinary speed. Our responsibility as physicians is to make certain that patient safety, clinical judgment and human accountability advance with it.<\/p>\n\n\n\n

About the Author<\/h3>\n\n\n\n

Philip Werthman<\/a>, MD, MMH, is a board-certified urologist and microsurgeon and Director of the Center for Male Reproductive Medicine<\/a> and Vasectomy Reversal in Los Angeles<\/a>. He has completed executive education in artificial intelligence and healthcare through Harvard Medical School.<\/p>\n\n\n\n

Selected Reference<\/strong><\/p>\n\n\n\n

U.S. Food and Drug Administration. Artificial Intelligence-Enabled Medical Devices. FDA.gov. Accessed September 2026.<\/a><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

By Philip Werthman, MD, MMH Artificial Intelligence Is Already Changing Medicine Artificial intelligence is no longer a futuristic concept in healthcare. It is already being used in hospitals, radiology departments, pathology laboratories, medical offices and research centers around the world. As a physician and surgeon, I wanted to understand not only what AI can do, … Continue reading Artificial Intelligence in Medicine, Clinical Decision-Making, and Why the Physician Must Remain in the Loop<\/span> →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3227","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.malereproduction.com\/blog\/wp-json\/wp\/v2\/posts\/3227","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.malereproduction.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.malereproduction.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.malereproduction.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.malereproduction.com\/blog\/wp-json\/wp\/v2\/comments?post=3227"}],"version-history":[{"count":1,"href":"https:\/\/www.malereproduction.com\/blog\/wp-json\/wp\/v2\/posts\/3227\/revisions"}],"predecessor-version":[{"id":3228,"href":"https:\/\/www.malereproduction.com\/blog\/wp-json\/wp\/v2\/posts\/3227\/revisions\/3228"}],"wp:attachment":[{"href":"https:\/\/www.malereproduction.com\/blog\/wp-json\/wp\/v2\/media?parent=3227"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.malereproduction.com\/blog\/wp-json\/wp\/v2\/categories?post=3227"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.malereproduction.com\/blog\/wp-json\/wp\/v2\/tags?post=3227"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}