Misdiagnosis: AI Can Help, But Let’s Get Real First

Jolly Nanda | June 4, 2025

Misdiagnosis is the annoying houseguest that refuses to leave modern medicine. Despite flashy new tech and miracle drugs, millions of patients are still affected every year. The sheer complexity of human health, a mess of fragmented medical records, and doctors juggling patients like a circus act, means even the best practitioners are set up for failure.

As someone who’s been swimming in the healthcare tech and patient advocacy pool for years, I’m calling it: we’re at a turning point. Artificial intelligence, fueled by real-time patient data that isn’t ancient history, has the potential to completely shake up how we spot and react to the unique SOS signals our bodies send. If we do this right, AI can break the misdiagnosis cycle and kickstart an era of personalized care that actually is.

The Shocking Stats on Diagnostic Errors

Diagnostic errors are a leading cause of preventable harm in healthcare. A 2022 study in Diagnosis revealed that nearly 800,000 Americans suffer permanent disability or death annually due to these errors. ¹ Government estimates suggest up to a staggering 12 million diagnostic errors happen each year in the U.S. ² The real kicker? Many of these aren’t due to some rare, complicated disease, but simply missing context: a forgotten drug allergy, a test result from a previous doctor collecting dust, a symptom from months ago that never made it to the physician’s radar. These aren’t edge cases; they’re becoming the norm as we get older, more complex, and bounce between specialists.

The Eye-Watering Cost of Getting It Wrong

Diagnostic errors drain the U.S. healthcare system to the tune of over $100 billion every year. ²  This number is misleading, as it is much higher when you add the impact of delay in care that misdiagnosis causes. That’s factoring in extra treatments, those hefty malpractice settlements, lost productivity, and long-term care. And the pain isn’t shared equally. Patients with chronic illnesses, the elderly, the uninsured, and those with complicated medical needs get the worst of it. They’re bouncing between providers, drowning in paperwork, and at huge risk of vital information just vanishing into thin air. This kind of fragmentation doesn’t just widen cracks in the system; it’s like a black hole swallowing people whole. Add to that the havoc it causes when trying to diagnose a rare disease. A 2024 study in the state of Pennsylvania reported a 9.5% increase from 2023.^{3 4}

The Organization for Economic Co-operation and Development (OECD), an intergovernmental organization with 38 member countries that promotes economic growth, prosperity, and sustainable development recently published a working paper stating that the direct consequences for diagnostic errors  on healthcare budget accounts for 17.5% of total healthcare expenditure. In the US, that would amount to $870B each year.⁵  If you are thinking that this is a US problem alone, let me assure you it is not. It is anticipated that 20-25% of the global population faces this issue. ²

The Problem with How We Do Things Now

Diagnosis has always relied on the doctor’s expertise and, let’s be honest, incomplete, and stale information. Medical records are often locked away in separate systems. Patient histories are spotty, especially if you’ve moved a lot or seen multiple doctors. And, crucially, patients aren’t usually encouraged to be active participants in figuring out what’s wrong.

Your body is constantly changing. The only person who knows what’s really going on is the person living in it. When we don’t capture the patient’s lived experience- the context, symptoms, and little details that you won’t find on a lab report, we miss vital clues. The result? Delayed or missed diagnoses and, sometimes, avoidable harm.

AI: Not a Crystal Ball, But a Powerful Tool

Everyone raves about AI’s ability to crunch massive datasets, but its real power in healthcare is pulling together clinical data with what patients are actually saying. When patients are actively involved, sharing symptoms and concerns, the data gets richer and more relevant. This lets AI flag potential diagnoses, rule out false alarms, and spot patterns that would otherwise be missed.

Now, hold on. It’s tempting to think AI is a magic fix for everything that’s wrong with healthcare. Newsflash: AI models are only as good as the information you feed them. Biases in data, systems that can’t talk to each other, and privacy concerns are all very real hurdles. There’s also the risk of relying too much on algorithms, which should never replace a doctor’s good judgment and bedside manner.

But, there’s growing evidence that AI-powered tools, especially those using real-time patient input, are helping doctors spot early signs of things like sepsis, cancer, and rare diseases. The key is that these systems work best when patients are partners, not just data points.

The Human Element: It’s Still About People

Preventing misdiagnosis requires more than fancy technology. It demands a fundamental shift in healthcare culture, from a “doctor knows best” approach to one where patients are actually heard. Transparency, trust, and genuine partnership is required.

Let’s face it, patients are more than just walking symptoms. It’s time we stop treating them that way! Give them the digital keys to their kingdom, their health data. Make it easy to access, simple to organize, augment, and safe to share. Because frankly, who else knows the full, messy, utterly human story of their health?

We also need to hold ourselves accountable for making sure AI systems reflect the diversity and complexity of the people they’re supposed to help. By combining AI’s analytical power with patients’ lived experiences, we can catch the things that would otherwise slip through the cracks and dramatically reduce the burden of misdiagnosis. This isn’t just about tech; it’s about putting people first.

Sources:

1. Newman-Toker DE, et al. (2022). Diagnosis. https://doi.org/10.1136/bmjqs-2021-014130
2. Newman-Toker DE, Peterson SM, Badihian S, et al. Diagnostic Errors in the Emergency Department: A Systematic Review [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2022 Dec. (Comparative Effectiveness Review, No. 258.) Introduction. Available from: https://www.ncbi.nlm.nih.gov/books/NBK588113/
3. Everylife Foundation for Rare Diseases (2023) Delayed Diagnosis Study. https://everylifefoundation.org/delayed-diagnosis-study/
4. Patient Safety trends in 2024. https://www.pslhub.org/learn/research-data-and-insight/data-and-insight/patient-safety-trends-in-2024-an-analysis-of-315418-serious-events-and-incidents-from-the-nation%E2%80%99s-largest-event-reporting-database-21-april-2025-r13222/
5. Slawomirski, L. et al. (2025), “The economics of diagnostic safety”, OECD Health Working Papers, No. 176, OECD Publishing, Paris, https://doi.org/10.1787/fc61057a-en.
6. Patient Safety in primary and outpatient health care. JFMPC https://journals.lww.com/jfmpc/Fulltext/2020/09010/Patient_safety_in_primary_and_outpatient_health.2.aspx