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Close-up of CT scan results reflected in glasses of physician.<\/p>\n
Universal Images Group via Getty Images<\/p>\n
Four months before Senator Lindsey Graham\u2019s death, I stood before the United States Senate<\/a> trying to answer a deceptively simple question: Where can artificial intelligence make the biggest difference in healthcare?<\/p>\n To illustrate my answer, I chose aortic dissection.<\/p>\n \u201cThe most dangerous failure is not a machine failure. It\u2019s a missed or delayed diagnosis.\u201d<\/p>\n This weekend, preliminary reports indicated that Senator Graham died from that very condition<\/a>.<\/p>\n The timing is an unfortunate coincidence. I have no knowledge of Senator Graham\u2019s medical care, and nothing written here should be interpreted as evidence that different technology would have changed the outcome. But this moment does remind us why conditions like aortic dissection have become defining examples in conversations about healthcare AI.<\/p>\n Aortic Dissection Highlights A Major Healthcare Challenge<\/p>\n I could have spoken about diabetes, heart failure or countless other diseases. Instead, I chose aortic dissection because it captures one of healthcare\u2019s hardest challenges.<\/p>\n The condition is uncommon. Its symptoms often resemble far more common illnesses<\/a>, including heart attack, stroke, severe back pain or abdominal pain. Recognition requires clinical expertise, but diagnosis is only the beginning. Once suspected, imaging must be interpreted quickly, specialists mobilized and patients frequently transferred for emergency surgery. Every minute matters.<\/p>\n Looking back, this example was never really about aortic dissection; it was about time. At least, that\u2019s what I eventually realized.<\/p>\n Healthcare AI is usually framed as a question of whether computers can recognize disease better than physicians. That is certainly an important area of innovation, but after more than 20 years helping health systems deploy clinical technology, I\u2019ve become convinced that recognition is only the first step.<\/p>\n I\u2019ve met clinicians at every stage of their careers and every stage of burnout. I\u2019ve never met one who wanted to miss a diagnosis. That\u2019s why I think we sometimes look for failure in the wrong place.<\/p>\n How Time And Communication Influence Outcomes<\/p>\n Healthcare is an extraordinarily human system. Radiologists finish shifts. Emergency physicians move on to the next trauma. Specialists focus on the problem they were asked to solve. Patients go home overwhelmed, trying to make sense of everything that happened while simultaneously figuring out how they\u2019ll get their children to school the next morning, who will take an aging parent to an appointment or whether they\u2019ll be able to return to work. Information isn\u2019t usually lost because someone intended for it to disappear. It gets buried beneath the realities of everyday life.<\/p>\n Months before I testified, Dr. Andrew Ibrahim, chief clinical officer at Viz.ai, appeared before the<\/a> House Energy and Commerce Committee to discuss healthcare AI. His testimony echoed what I hoped lawmakers would understand. \u201cWhat matters,\u201d he explained, \u201cis not the algorithm in isolation, but how these tools are integrated into real clinical workflows to solve real, high-stakes problems.\u201d<\/p>\n For me, the most memorable part of Ibrahim\u2019s testimony was an experience he shared about his father.<\/p>\n When his father developed stroke symptoms, Ibrahim recognized what was happening because of his clinical knowledge. He alerted the emergency department before his father arrived and coordinated the care that ultimately helped produce a good outcome.<\/p>\n When we spoke recently, Ibrahim told me that the experience revealed something he had not fully appreciated even during his medical training. \u201cI didn\u2019t appreciate how hard it is for information to get to the right person,\u201d he said. \u201cThere\u2019s a person at the end of that telephone game who has to make a time-sensitive decision.\u201d<\/p>\n The opportunity for AI, he explained, is not only to recognize a potentially life-threatening condition but to move the relevant images and information through that chain to the specialist who must act.<\/p>\n His testimony made a related point that extends far beyond stroke.<\/p>\n \u201cNot everyone has a medically trained son ready to coordinate their emergency care. We need systems that ensure patients get the right treatment without relying on luck or personal connections.\u201d<\/p>\n That\u2019s the sentence I haven\u2019t been able to move beyond. We\u2019ve spent years asking whether AI can diagnose disease better than physicians. I\u2019ve come to think AI is at its best when it makes extraordinary outcomes less dependent on extraordinary luck.<\/p>\n What Better Looks Like<\/p>\n Imagine a patient undergoing imaging ordered for kidney stones. The radiologist notices a small spot on the lung unrelated to the original reason for the exam and appropriately recommends follow-up. The urologist remains focused on the kidney stone. Months later, the patient is involved in a car accident and undergoes another CT scan. The spot is still there. It has grown. Again, the radiologist recommends follow-up, and again the emergency team appropriately focuses on treating the injuries that brought the patient to the hospital.<\/p>\n By the time the patient leaves the emergency department, they\u2019re exhausted and overwhelmed, not thinking about pulmonary nodules.<\/p>\n Life moves forward.<\/p>\n The patient drives their child to school. They care for an aging parent. Work fills the calendar again. The recommendation quietly disappears beneath everything else life demands.<\/p>\n Years later, unexplained pain leads to another scan. This time the diagnosis is devastating: advanced cancer. The person who once drove their children to school is now too weak from chemotherapy to drive themselves. Their aging parent now drives them to treatment.<\/p>\n Nobody intended this outcome. Nobody ignored the patient. The system simply behaved exactly the way systems built around busy human beings tend to behave. <\/p>\n Now imagine the same patient in a healthcare system designed differently.<\/p>\n The radiologist still identifies the lung spot. The physician still exercises clinical judgment. The patient still leaves the emergency department thinking about tomorrow instead of an incidental finding.<\/p>\n Life still moves forward.<\/p>\n The patient still drives their child to school. They still care for an aging parent. They still forget.<\/p>\n The difference is that the healthcare system doesn\u2019t forget with them.<\/p>\n The spot measurement flows automatically into the report. Evidence-based follow-up recommendations are generated. The patient leaves with not only a recommendation but clear guidance about what happens next. When recommended imaging never occurs, the system recognizes that the patient has been lost to follow-up and reaches out through the patient portal. When that doesn\u2019t work, another message is sent through the primary care office. An appointment is scheduled.<\/p>\n At follow-up imaging, the spot has progressed, but it is found while still curable. Treatment is straightforward.<\/p>\n No chemotherapy. No radiation. No major surgery. The patient keeps driving their child to school. They keep taking their parent to appointments.<\/p>\n Life keeps moving forward.<\/p>\n