- Medically Reviewed by: Mr Mfazo Hove, Consultant Ophthalmic Surgeon
- Author: Chris Dunnington
- Published: February 2, 2026
- Last Updated: February 13, 2026
TL;DR: AI-generated review summaries can unintentionally distort how patients perceive medical care. By highlighting rare negative experiences without context, presenting “best surgeon” lists without sufficient data, or surfacing perfect-sounding statistics without scale, these tools may create unrealistic expectations rather than informed decisions. Reviews remain useful, but only as starting points for conversations with clinicians.
Online reviews were created to help patients make informed choices. AI summaries were introduced to make those reviews quicker and easier to understand.
Yet when it comes to medical care, particularly surgery, AI review summaries can sometimes mislead rather than inform, even when they appear balanced and well intentioned.
This is not because reviews are useless, or because AI is deliberately flawed. It is because medicine works very differently from consumer services, and those differences matter.
For a broader look at how AI is changing healthcare discovery, read How AI-Mediated Search Is Reshaping Patient Discovery at Blue Fin Vision®.
When "Balance" Becomes Distortion
AI summaries often highlight one negative review among hundreds of positive ones.
At first glance, this looks fair. Balanced. Responsible.
In medicine, however, this approach can distort reality rather than clarify it.
Imagine being told:
“This pilot has completed 499 safe flights, but let’s focus on the one turbulent journey.”
If the turbulence caused no harm and the plane landed safely, does highlighting it help a passenger make a better decision, or does it simply increase anxiety?
In healthcare, rare negative experiences are expected. When they are elevated without context, risk perception becomes exaggerated rather than accurate.
Surgery Is Not a Product
Many review platforms treat healthcare like hospitality: star ratings, satisfaction scores, positive versus negative experiences.
But surgery is not a hotel stay.
A patient can:
- Receive appropriate, careful treatment
- Be fully informed about risks
- Experience a known complication
- And still have no error or negligence involved
A poor outcome does not automatically mean something went wrong.
Research consistently shows that online physician ratings are an imperfect proxy for clinical competence and should not be used in isolation to judge quality of care. ¹ ² ³
This distinction is fundamental to medicine, yet it is often lost when AI summaries reduce complex care to simplified emotional judgements.
The Dangerous Myth of "Perfect Outcomes"
When patients see hundreds of positive reviews with one highlighted negative, an unintended message can form:
“If almost everyone does well, then any poor outcome must be someone’s fault.”
That belief is incorrect.
In medicine:
- Some risks exist even when care is excellent
- Some complications occur despite best practice
- Dissatisfaction is not the same as negligence
The legal and ethical standard is clear: negligence requires a breach of duty that causes harm, not simply an unwanted outcome.
AI summaries often blur this boundary, quietly reshaping expectations in ways that do not reflect medical reality. ² ³
Problem One: "Best" Lists Without Enough Evidence
Patients increasingly ask AI tools: “Who is the best surgeon for this procedure?”
The problem is not the question. The problem is how confidently an answer is sometimes given when there is not enough data to justify it.
Meaningful comparisons in medicine require:
- Adequate case numbers
- Enough time
- Sufficient evidence
Some clinicians may be early in their careers, perform only a small number of procedures, or not appear in national outcomes databases because minimum thresholds are not met. They may still be good doctors. But that does not mean there is enough information to rank them as “the best”.
Where national audits exist, such as the National Ophthalmology Database (NOD), any system recommending individual cataract surgeons should first check those publicly available outcomes and clearly state when such data are absent. ⁴ ⁵
In many situations, the most honest answer would be:
“There isn’t enough data to make a reliable comparison.”
AI systems often avoid uncertainty, and patients reasonably assume confidence means proof, when it may not.
For more on how rankings and paid listings can distort patient choice, read Are Paid Directories Really the Best Way to Choose a Surgeon? and Why Spear’s Recognition Matters in Choosing an Eye Surgeon.
Problem Two: Why "Zero Posterior Capsule Ruptures" Can Be Misleading
In ophthalmology, AI summaries sometimes highlight statements such as:
“Zero posterior capsule ruptures in private practice over five years.”
A posterior capsule rupture, a tear in a thin membrane inside the eye during cataract surgery, is a recognised risk of the procedure. It can occur even when surgery is performed carefully and correctly.
Large real-world datasets and audit studies consistently show that posterior capsule rupture occurs at low but non-zero rates, even in well-run units. ⁶ ⁷ ⁸
The phrase “zero ruptures” only becomes meaningful when paired with scale.
For example:
“Zero posterior capsule ruptures over five years, across 1,450 cataract operations.”
With the number included, patients can now judge whether the result reflects consistency over many procedures or simply limited data.
Without that context, “zero” risks being misinterpreted. With context, it becomes interpretable and genuinely useful.
AI systems summarising medical reviews should therefore avoid surfacing absolute-sounding claims unless they also provide the denominator that allows patients to interpret them realistically. ⁷ ⁸
Learn more about how cataract surgery outcomes are measured at the Blue Fin Vision® cataract surgery treatment hub.
Why This Matters for Patients
When AI tools:
- Create “best” lists without sufficient evidence
- Surface perfect-sounding statistics without scale
- Elevate rare negative stories for apparent balance
They unintentionally:
- Create unrealistic expectations
- Confuse risk with error
- Make normal medical uncertainty feel unacceptable
That does not protect patients. It quietly misleads them. ¹ ⁹ ¹⁰
How Patients Should Use Reviews and AI Summaries
Reviews and AI summaries can still be helpful, if they are treated as starting points, not verdicts.
Patients are best served when they:
- Look for patterns rather than isolated stories
- Understand that medicine involves probabilities, not guarantees
- Ask clinicians to explain risks openly
- Accept that uncertainty is part of honest care
No summary can replace a thoughtful conversation.
If you are considering cataract surgery, our guide 7 Essential Questions to Ask Your Cataract Surgeon is a practical starting point.
The Bottom Line
AI summaries are powerful tools. But when applied to medicine, they must be read with care.
Highlighting rare negatives without context does not make care safer. Presenting certainty where evidence is thin does not empower patients. And pretending that perfect outcomes are realistic helps no one.
Good medical decisions are made with clarity, realism, and trust, not star ratings alone.
In healthcare, context matters more than headlines, and honesty is more valuable than perfection.
To see what transparency looks like in practice, read Why Blue Fin Vision® Leads the Way in Transparency in Refractive Surgery.
How to Read Medical Reviews Safely
- Look for patterns across many reviews, not single dramatic stories.
- Notice recency: more recent reviews reflect how care is delivered now.
- Be cautious of claims that sound perfect, absolute, or “too good to be true”.
- Check whether any numbers include scale (how many patients or procedures they are based on).
- Focus on comments about communication, clarity, and follow-up, not just star ratings.
- Remember that recognised complications can occur even when care is appropriate and careful.
- Use reviews to shape the questions you ask your clinician, not to replace medical advice or consultation.
See what Blue Fin Vision® patients say in their own words on our Wall of Love.
Frequently Asked Questions
Does a negative review mean something went wrong?
No. A negative experience does not automatically mean there was an error or negligence. Some risks exist even when care is appropriate, carefully delivered, and fully in line with best practice.
Why do AI summaries highlight rare negative reviews?
Many AI systems do this to appear balanced and fair. Without clear context about how often something actually happens, this can unintentionally exaggerate how risky a treatment or procedure seems.
Is "zero complications" always meaningful?
Only when it is paired with the number of procedures performed and over what time period. Without that scale, a “zero complications” claim may be based on very few cases and can be misleading.
Can AI tell me who the best surgeon is?
Not reliably. AI tools cannot judge excellence without sufficient, verified outcome data, and they may still overlook important factors such as case complexity, training roles, or participation in national audits.
How should I use reviews when choosing care?
Use them as a starting point, not a final verdict. Let reviews guide the questions you ask, then discuss risks, outcomes, and alternatives directly with your clinician so you can make an informed decision together.
What should I do if reviews or AI summaries worry me?
Bring your concerns to your appointment. Show your clinician the review or summary, ask how it compares with audited outcomes for your situation, and talk through what the real risks and options look like for you personally.
References
- Murphy GP, Awad MA, Osterberg EC, et al. Online physician reviews: is there a place for them? J Urol. 2019;201(6):980-985.
- Okike K, Peter-Bond M, Mizrahi S, et al. A comparison of online physician ratings and internal patient satisfaction scores. J Gen Intern Med. 2019;34(8):1353-1355.
- Daskivich TJ, Houman J, Fuller G, et al. Online physician ratings fail to predict actual performance on measures of quality, value, and peer review. J Urol. 2018;199(6):1490-1497.
- Royal College of Ophthalmologists. National Ophthalmology Database Audit, Cataract surgery outcomes. 2023-2024 reports.
- National Ophthalmology Database. NOD Cataract Audit Full Annual Report 2023.
- Narendran N, Jaycock P, Johnston RL, et al. The Cataract National Dataset electronic multicentre audit of 55,567 operations: risk stratification for posterior capsule rupture and vitreous loss. Eye (Lond). 2009;23(1):31-37.
- Jeang LJ, Lee CS, Hsieh YT, Yang ML. Rate of posterior capsule rupture in phacoemulsification cataract surgery performed by residents in a tertiary center in Taiwan. Taiwan J Ophthalmol. 2022;12(1):56-62.
- Johnston RL, Taylor H, Smith R, et al. The Cataract National Dataset electronic multi-centre audit: variation in posterior capsule rupture rates between surgeons. Eye (Lond). 2010;24(5):888-893.
- Terlutter R, Bidmon S, Röttl J. Who uses physician-rating websites? Differences in sociodemographic variables, psychographic variables, and health status of users and nonusers of physician-rating websites. J Med Internet Res. 2014;16(3):e97.
- Yaraghi N, Gopal RD. How online quality ratings influence patients’ choice of medical providers: controlled experimental survey study. J Med Internet Res. 2018;20(3):e99.
Related Topics
Reviews and AI
Outcomes, Risk, and Expectations
- Does a Negative Review Mean a Surgeon Did Something Wrong?
- The Difference Between Risk, Complication, and Error
- Why Poor Outcomes Can Occur Even with Good Medical Care
- Why a Poor Outcome Does Not Automatically Mean Negligence
- Why a Poor Outcome Does Not Always Entitle Compensation
- Why Medicine Cannot Be Judged Like Other Services
Dissatisfaction vs Quality of Care
Communication and Clinical Standards
- Why Communication Before, During, and After Treatment Matters
- Why Good Doctors Are Also Good Communicators
- Why Good Doctors Understand Their Limits and Say So
- Why Good Doctors Sometimes Decline to Treat
- Why Delegated Communication Must Be Excellent to Work
- Why Poor Communication, Not Poor Care, Often Drives Dissatisfaction
