Why Even Experienced Doctors Miss Diagnoses - AI Detects What Humans Overlook
Even experts can miss warning signs. See how AI steps in to catch critical diagnoses and protect lives.
A Story That Shouldn't Surprise You
Consider a patient who presents to an emergency room with the signs of chest pain and shortness of breath. The physician who is present, an experienced person of 15 years takes 10 minutes with them. The symptoms resemble those of anxiety when the patient is stressed, after all. The EKG is normal looking. "Take some deep breaths. You will be all right, the doctor tells them.
Six hours on, the patient becomes unconscious. It wasn't anxiety. It was a blood clot in the lungs and a pulmonary embolism. One of the conditions that, left unnoticed, claims 1 out of every 3 individuals.
It is not a novel of an irresponsible physician. It is a tale of how the human brain, even a professional one, can malfunction in the appropriate (or inappropriate) situations.
The reality is disturbing: almost 1 out of 4 patients, who are seriously ill and are hospitalized, receive a delayed or missed diagnosis. And the results are disastrous. Viewed another way, a disease was misdiagnosed, resulting in the death or permanent disability of an estimated 795,000 Americans annually, which is quite large compared to the death tolls of car accidents, opioid overdoses, or hospital-acquired infections combined.
But herein is where artificial intelligence comes in. Not to take the place of doctors, but as an unwearied partner, one who is able to see patterns that humans can not, and which you will weary, has never been weary, has never been in a hurry, and has never lost a shadow which shows up on an X-ray.
The Hidden Culprit: Why Smart Doctors Make Diagnostic Mistakes
Let's address the elephant in the room: This isn't about incompetence. Most diagnostic errors don't happen because doctors lack knowledge. They happen because of how the human brain naturally thinks and medicine amplifies this problem.
Cognitive Bias: The Invisible Enemy
Cognitive biases are mental shortcuts that are utilized by our brain in the process of rapid information processing. They come in handy in our life. In medicine, they can kill.
The most hazardous is Premature Closure. A doctor creates an initial impression and ceases to seek alternatives. Suppose a patient comes to the doctor with pain in the abdomen and the physician concludes that it is gastritis. They stop digging. What they fail to examine: The patient as well is vulnerable to a ruptured appendix or vascular emergency. When someone discovers the actual issue it is already too late as hours of time are lost.
Anchoring Bias refers to the situation when doctors become obsessed with initial information and find irrelevant refuting information. One of the patients who has had an experience with panic attacks arrives with chest pain and the physician anchors it to anxiety so much that the warning signs are ignored.
Confirmation Bias causes doctors to look for evidence to confirm their original diagnosis and disregard contradictory information. An inconvenient lab finding? Maybe it's a lab error. Abnormal vital signs? Most likely, an error of measurement.
These aren't character flaws. Research has indicated that cognition is applicable in 74% of all internal medicine diagnostic errors. Three out of four.
The System Hasn't Caught Up
In addition to the personal bias, healthcare systems are the optimal setting of the diagnostic errors:
a. Time pressure: Radiologists read 100+ imaging scans in a shift. There is a staffing shortage in emergency rooms. Patients meet specialists for 15 minutes.
b. Lapses in information: The medical records are disjointed. Previous test scores do not trail patients. One physician is not aware of what another has found out.
c. Fatigue and burnout: Overnight work, decision fatigue and cognitive overload undermine even the most optimal clinicians.
d. Rarity is a diagnosis killer: A rare diagnosis is one that produces typical symptoms.
The brain of the doctor is biased to common things that are common - but it fails to notice the rare zebra among the herd.
The most tragic part? Approximately, 85% of the detrimental diagnostic mistakes can be avoided. Had one additional check been added to the system and to the thinking process of the doctor, such a high number of these deaths would have been prevented.
The Scope of the Problem: Numbers That Matter
To make this real to us, consider the following data:
The three most common diagnostic harmful illnesses that constitute half of all diagnostic damage:
Stroke: The most commonly misdiagnosed disorder, which causes 17.5% of all severe harms, due to missing diagnosis.
Sepsis: It is frequently confused with ordinary infections; time-lapse is lethal.
Pneumonia: It is often incorrectly diagnosed with bronchitis or other breathing problems.
In the fifth position also: Venous thromboembolism (blood clots) and Lung cancer.
These five conditions are sufficient to cause 308, 000 serious harms every year in the U.S.
In addition to these high-level classifications, 12 million diagnostic errors happen annually in the United States-that is, this figure might be even greater, when undetected cases are also considered. The health care system incurs more than a hundred billion dollars every year to take care of the effects of these errors.
And a sobering fact here is: The price is not so much measured in dollars. It is quantified in years of life lost, lasting disability and shattered families.
Chapter of Artificial Intelligence: The Unwearied Diagnostician
So where does AI come in?
AI doesn't replace doctors. It does not arrive at final diagnoses. What it does is to remove the single largest bottleneck in diagnosis, the human propensity to overlook patterns in vision and get stuck on first impressions.
AI is most effective in the field of medical imaging. And the consequences are astounding.
Real-World Applications: AI When Doctors Mistake this
Lung Cancer Detection
An AI algorithm that was provided commercially was compared to a group of four seasoned thoracic radiologists who were viewing X-rays of the chest. The AI performed better on the first read in comparison to the human team. More to the point, it was able to identify 29 percent of all lung nodules which were at first missed by human radiologists- nodules, which, in the absence of their detection, would develop into severe cancers.
Elsewhere, detection algorithms based on lung cancer were able to detect the cancer in up to 98.7 percent accuracy, identifying small suspicious spots that the human eye may overlook as either scar tissue or shadows.
Breast Cancer Screening
AI-assisted breast cancer detection cut false positives by 37.3 percent - that is, fewer women got false beliefs that set them into a state of anxiety due to a benign finding. More importantly, it indicated 49.8 percent of interval cancers that were overlooked by human radiologists at the first instance. AI has been able to identify cancers that human eyes have failed to detect.
Melanoma and Skin Cancer
In a meta-analysis of 38 studies, AI was as sensitive (0.86) and specific (0.94) as dermatologists in controlled environments, and it was as effective at diagnosing melanoma as dermatologists are.
Diabetic Retinopathy
FDA-approved in 2018, the IDx-DR system uses retinal photographs to identify signs of diabetic-related eye damage that may result in blindness in under a minute with 87% sensitivity and 90% specificity- allowing a non-specialist to screen for diabetic-related eye damage.
Pneumonia Detection
The COVID-19 and other pneumonias were analyzed by AI systems on the basis of the chest X-rays; the accuracy was 93 percent, and the analysis took less than 10 seconds per image. In the clinical practice, this implied that patients were put under antibiotics, or ventilator support hours prior to how it would have been done manually.
Multiple Sclerosis Diagnosis:
Evidence of artificial intelligence machines that scan the brain using MRI enhanced the accuracy of the diagnostic results by 44 percent and reduced the time taken to read the diagnostic results by half. This is life-altering in case of a progressive disease, where early detection alters the prognosis.
Why AI Works So Well at Diagnosis
It isn't magic. It is stability and scale pattern recognition:
No fatigue: AI works the same at 3 AM when on an overnight shift as at 9 AM. The accuracy decreases significantly when radiologists have their 60th image and onward.
No cognitive bias: AI is not able to anchor on a first impression. It uses the same line of reasoning to analyze every image.
Pattern mastery: AI is trained on a thousand or millions of labeled images. It has been able to see all the degrees of a disease much more than any individual human being.
Speed: The analysis which would have required a radiologist 5 minutes is completed within seconds, enabling the division of work and prioritization of emergency cases.
Stability among practitioners: Detection algorithm of Algorithm A works identically in London, Lagos and Los Angeles. The accuracy of human diagnosis is all over.
The Joint Venture That Prospers: AI + Human Judgment
This is what the research has found regarding the optimal model: AI is most effective in working together with experienced clinicians.
Doctors provided with standard AI predictions with explanations showed a 4.4-point improvement over baseline in diagnostic accuracy. In case the AI was biased or unreliable it actually reduced the accuracy by 11.3%-an important reminder that not all AI is created equal, and human skepticism is important.
AI is no longer needed as a decision-maker; it is a decision-support tool: the sweet spot:
AI raises flags on abnormalities and priorities cases.
Radiologists verify and offer clinical circumstances.
Physicians combine the imaging findings with the history of the patients, their symptoms and examination.
Human expertise + machine consistency are combined to make better diagnoses.
Actual Hospitals doing this currently.
The adoption is increasing. By 2024, 71% of hospitals were utilizing AI-driven predictive technologies compared to 66% in 2023. Huge hospitals are almost at 96% adoption. Even smaller hospitals are now applying AI to minimize the no-show rates, anticipate the readmissions, and identify high-risk patients.
The AimSG platform in Singapore decreased the turnaround times of chest X-rays by 22% by sending complex cases to the right place and signaling urgent results. SwiftMR minimizes the scan times per MRI by half in Korea, maximizes hospital income, and also shortens the duration of patient waiting time. In China, AI doctors employed in Agent Hospital work with diagnostic cases in 21 specialties with an accuracy of 93% on more than 10 common disorders.
They are not futuristic experiments. They are the functional systems enhancing patient safety in the current times.
The Limitations (Because Perfect Doesn't Exist)
Being frank about the limits of AI:
What AI struggles with:
Incomplete data: X-ray with no history is more difficult to read. AI is most effective in the context.
Abnormal manifestations: Diseases do not necessarily take the textbook. AI is not effective in unusual cases and uncommon situations.
Combination with patient care: AI is genius at identifying patterns but is unable to complete a physical examination, is unable to comfort an anxious patient, is unable to modify a diagnosis based on the feelings of a patient.
Generalization: An AI that is trained on the population of Europe will have a different result with the population of Asia following minor differences in anatomy and disease manifestation.
The essential need: AI needs human supervision. An unbalanced algorithm or the one trained on bad data will be able to be in error consistently- possibly more than randomly.
The Cognitive Bias Doctors Can Overcome (Even Without AI)
We should also not end without something of importance, so before we end, you need not have AI to minimize diagnostic errors. But it helps.
Even conventional measures do not fail:
"Think Twice" Protocol: Once you have made a first diagnosis, stop and question, What else could this be? This brief meditative scene is interrupted.
Systematic Review of Alternatives: Don't only stop at diagnosis. Write down 3-5 possible alternative explanations and argue them out.
Team-Based Diagnosis: Canonical Reduction: The second opinion of an expert eliminates most anchoring bias. Cognitive errors are caught in the diverse perspectives.
Standardized Checklists: When hospitals use diagnostic checklists, there is a reduction of missed diagnosis by 40% of hospitalized patients through ordering of all vital tests.
Better Communication: Diagnostic accuracy is improved when feedback by specialists is sent to the primary care physicians, and when patients are actively listened to.
The issue is that these need time, change of systems and culture. AI accelerates the solution.
The Future: AI as a Safety Net of healthcare
Misdiagnoses will not be eliminated. Medicine is unpredictable in nature. However, 7,95,000 avoidable deaths and irreversible disabilities every year is a solvable issue with the appropriate tools.
AI isn't replacing doctors. However, an AI physician is superior to either. The algorithm gives the radiologist two sets of eyes when he is too tired to read the 80 chest X-rays. The time-stressed emergency doctor receives an approximate indication of a pulmonary embolism within 30 seconds. Highlighting of suspicious areas is done to the surgeon checking pathology slides and they could have missed.
In the next five years, expect:
Multimodal AI: Systems which combine images, laboratory findings, genetics, and clinical records into integrated diagnostic suggestions.
Real-time Clinical Decision Support: Autopopulated notices in cases where the patient satisfies the diagnostic criteria of a rare disease.
Specialist-In-a-box: AI democratizing the diagnosis of experts in rural and underserved regions.
The future is evident: AI-enhanced diagnosis is the direction that medicine takes. Whether this will or will not happen is not relevant, but the rate at which hospitals, regulators and insurance systems can be adjusted.
What It Means to You (As a Patient or Caregiver)
As a patient: If it is a serious diagnosis, request a second opinion. Back off when it is not right. Ask imaging assessments or expert contributions. Your incapacity may make a mistake that can claim lives.
In case you are in healthcare: Be the proponent of AI adoption in your organization. Not instead of clinical judgment, but as a backup. The tool exists. The evidence is clear. What's missing is adoption.
And in case you are wondering about this space: AI diagnostic tools will appear in your local healthcare system. They are coming--and they are quietly saving lives all over the world in hospitals.
The Bottom Line
The best doctors commit diagnostic errors. They are not thoughtless because they are human. The failings in human reasoning are predictable due to cognitive biases, time pressure, fatigue, and incomplete information.
These failures are not removed by artificial intelligence. But it catches many of them.
The future of healthcare is neither human nor machine. It is collaborating with each other, in the pattern recognition and consistency of AI, and the clinical judgment, empathy, and wisdom of trained physicians.
And to the 795, 000 Americans who succumb or permanently lose their abilities annually because of the mistakes made in their diagnosis, that cooperation cannot come soon enough.
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