The history of medical diagnosis is one of meticulous observation. Ancient Egyptian physicians were the first to diagnose urinary tract infections by observing a patient’s urine patterns. To diagnose heart and lung diseases, medieval physicians began to measure the pulse, a core element of the physical exam. Palpation Percussion. The 20th century added laboratory studies, and the 21st century saw the arrival of advanced imaging and genetics.
But despite advances, diagnosis remains largely a human endeavor, with doctors relying on so-called disease scripts — collections of signs, symptoms, and diagnostic findings that characterize a disease. Medical students spend years memorizing such scripts, training to identify sub-millimeter variations in electrocardiogram measurements that could, for example, warn of a heart attack.
But humans make mistakes. Sometimes a misdiagnosis occurs because a doctor misses something. The disease pattern progresses as expected, but the doctor misinterprets that expectation. This is because 15% to 20% Misdiagnosis can also occur when illness characteristics don’t match known patterns, such as a heart attack occurring without obvious symptoms or ECG findings.
If given sufficient funding support and implemented properly, artificial intelligence can help solve these two fundamental problems.
First, AI is less susceptible to common factors that lead doctors to make diagnostic errors. Malaise, Limited time and cognitive resources when treating large numbers of patientsGap knowledge And dependency A spiritual shortcutEven when a disease fits the script, computers can be better than humans at identifying details buried in mountains of medical data.
Leveraging AI to improve the accuracy and timeliness with which doctors recognize illnesses can mean the difference between life and death. For example, ischemic stroke is a life-threatening emergency in which an artery becomes clogged, preventing blood flow to the brain. Brain imaging confirms the diagnosis, but those images must be taken and interpreted quickly and accurately by a radiologist. Research shows that AI can identify strokes within seconds of being taken, thanks to superhuman pattern-matching capabilities. Several tens of minutes They can make diagnoses faster than busy radiologists. Sepsis, pneumoniapulmonary blood clots (Pulmonary embolism), Acute kidney injury and other terms.
Secondly, computers could be useful for diseases for which adequate treatments have not yet been developed. AI can actually diagnose diseases using new patterns that are hard for humans to distinguish. Take hypertrophic cardiomyopathy, for example, a rare genetic disorder that causes the heart muscle to grow larger than it should, eventually leading to heart failure and death. Experts say: Only 20% of people affected are diagnosedIt’s a process that requires a consultation with a cardiologist, a cardiac ultrasound, and often genetic testing.So what about the other 80%?
Researchers from across the country, Mayo Clinic and University of California, San Francisco have demonstrated that AI can detect complex, previously unrecognized patterns and identify patients who may have hypertrophic cardiomyopathy, meaning that AI-driven algorithms could potentially screen for the disease during routine ECG testing.
AI was able to recognise these patterns by looking at electrocardiograms of people with and without the disease. The rapid increase in healthcare data such as detailed electronic health records, images, genomic data, biometrics and behavioural data, coupled with advances in artificial intelligence technology, has created a huge opportunity. With its unique ability to identify patterns from data, AI has assisted radiologists. Finding hidden cancerto pathologists Characteristics of Liver Fibrosis The ophthalmologist Detecting retinal disease.
One challenge is that AI is expensive, requiring large amounts of data and technology to train computer algorithms. As these resources become more widespread, the associated intellectual property It is difficult to protect, and private investment is being stifled. Diagnostics in general has long been considered an unattractive investment. Unlike the therapeutic sector, which has an estimated $300 billion in R&D expenditures, investment Diagnosis is understated over the years $10 billion With private funds.
Additionally, there is the question of who will pay for the use of AI-based tools in healthcare. Some applications, such as stroke detection, save insurers money (by preventing costly ICU stays and subsequent rehabilitation); these technologies tend to be reimbursed more quickly. But other AI solutions, such as hypertrophic cardiomyopathy detection, may lead to increased spending on costly downstream therapies to treat the newly identified chronic condition. In these cases, the use of AI has the potential to improve quality of care and long-term outcomes, but without financial incentives for insurers, reimbursement and adoption may be slow.
Life science companies rarely Assist Development of AI-based diagnostics and reimbursement: This could help fill the gap, but may require the federal government to play a larger role. COVID Diagnosis Support Rapid development of critical tests spurred during the pandemic, Cancer Moon Shot Project Contributing to the advancement of research and development In screening and new treatments.
Funding on the scale needed to advance new medical frontiers is typically difficult to come by. But the National Academy of Medicine Estimation Improved diagnostics in medicine could save tens of billions of dollars and countless lives.
Artificial intelligence shows us the way there. It should complement, not replace, the human expertise that is already saving so many lives. The future of medical diagnostics is not about handing over the keys to AI, but rather leveraging AI capabilities that humans cannot. If we invest enough and do it right, this could be a special moment for diagnostics.
Gaurav Singhal is a computer scientist and physician at Harvard Medical School. and He previously served as Chief Data Officer at cancer diagnostics company Foundation Medicine. Anupam B. Jena is an economist, physician, and professor at Harvard Medical School.Random acts of medicine: The hidden forces that motivate doctors, affect patients, and shape our health.“And that Random Acts of Medicine Substack.
