AI Hallucinations in Clinical and Pharma Practice: The Hidden Cost of Confident Error
Artificial intelligence is now embedded across clinical, pharmaceutical and healthcare workflows but its most underestimated failure mode remains hallucination, the generation of plausible sounding content that is unsupported, inaccurate or entirely fabricated. In high stakes settings hallucination is not merely a technical defect. It is a clinical operational and governance risk because it can appear with confidence, move quickly through workflows and influence decisions before it is detected.
The danger is especially acute in medicine because hallucinated outputs are often not obviously absurd. They can sound structured, authoritative and internally coherent which makes them more likely to be trusted by busy clinicians, medical writers and Pharma teams. That trust is the hidden cost of confident error.
Where hallucinations emerge
Hallucinations can appear anywhere AI is used to summarise, classify, recommend or infer. In clinical care they may arise in consultation summaries discharge instructions triage support radiology support or diagnostic decision support. In Pharma they can emerge in medical information drafting, literature synthesis, adverse event triage, protocol comparison, safety review and trial feasibility analysis.
One of the most consequential settings is oncology trial matching. AI systems increasingly help screen patients against eligibility criteria, identify candidate trials or prioritise records for human review. Because these systems often rely on fragmented notes, free text and complex protocol language hallucinations can be especially harmful. A model may infer eligibility that does not exist or exclude a patient who should have been considered.
A less discussed pain point
The least discussed but most important pain point is upstream exclusion. In many workflows hallucinations do not end with a wrong recommendation. They prevent a patient from ever reaching a human reviewer. That means the damage is invisible. No one may notice that the AI silently filtered the patient out before the final decision stage.
This is especially problematic in cancer trial access. If an AI tool falsely concludes that a patient fails a biomarker or prior therapy requirement the trial team may never see the patient’s record. That is not just a documentation error. It is a hidden access failure.
Documented examples of harm
Published examples show that hallucination can create real clinical and scientific harm. In one widely cited medical example summarised in the literature ChatGPT fabricated references when asked about homocystinuria associated osteoporosis and late onset Pompe disease generating papers and PubMed IDs that did not match the cited content. In another documented example Bard generated convincing but nonexistent book titles when asked to produce a reading list illustrating the broader errors with confidence.
A more clinically relevant study tested LLMs with vignettes containing planted errors and found that the models repeated or amplified those errors in up to 83% of cases. This is critical because clinical workflows often involve exactly this kind of input: imperfect notes copied forward text or chart data containing existing inaccuracies. In other words AI may not only invent errors. It may amplify pre-existing errors at scale.
In a separate clinical documentation framework, study hallucinations were found in 1.47% of generated sentences but 44% of hallucinations were major meaning they had the potential to affect diagnosis or management if left uncorrected. The same study found omissions were more common overall at 3.45% but hallucinations were more likely to be clinically severe. That distinction matters. Frequency alone understates risk.
Model performance and hallucination rates
Hallucination rates vary substantially by task and benchmark. In one review of medical hallucination research a clinician survey found that over 90% of respondents had encountered medical hallucinations from AI and about 85% believed they could cause patient harm. In the same research summary a large-scale evaluation across eleven foundation models found that even medical specialised models remained vulnerable to hallucination and general purpose models sometimes performed better on hallucination free response rates.
Task specific rates provide a more reliable picture than broad brand level claims. In a medical reference generation study hallucination rates were 39.6% for GPT 3.5 28.6% for GPT 4 and 91.4% for Bard when producing references in response to medical prompts. In a clinical note generation framework hallucination rate was 1.47% overall but with high severity errors concentrated in the plan section.
The key point is not that one model is safe and another is unsafe. The key point is that hallucination rates are context dependent and can change dramatically based on whether the task is diagnosis, prediction, summarisation, citation generation or chart review.
Major companies using AI for trial matching
Major companies are already using AI to improve clinical trial matching. Tempus reported screening out approximately 72% of ineligible patients and increasing potential trial matches by 27.31% after nurse review. Tempus also reported 33,083 reviewed matches, 6,040 patients passed to sites for interventional trials and 1,312 for observational studies through its matching workflow.
These are encouraging numbers but they should not be read as proof that AI is solved. In a separate AI enabled clinical trial notification study physician review rejected 17.10% of AI suggestions due to false positives showing that model output still requires careful human validation. In oncology trial recruitment more broadly ESMO notes that AI powered matching alone will not solve recruitment because the workflow remains constrained by protocol complexity and real world implementation barriers.
Other AI driven matching systems including patient facing trial search tools such as TrialGPT are also designed to accelerate discovery and shorten time to match. Yet the same pain points remain. In a NIH study clinicians using TrialGPT spent about 40% less time screening patients while maintaining similar accuracy to manual review. That is a meaningful efficiency gain but it still depends on human oversight and does not eliminate the risk of false positives or false negatives.
How many trials have failed?
There is no defensible published percentage showing how many cancer or Pharma trials have failed specifically because of AI hallucination. That number is not well established in the literature. What can be said is that AI errors can reduce trial matching accuracy, delay enrolment and distort feasibility assessment which may indirectly affect recruitment performance and operational efficiency. However, claims that a specific share of trials have failed because of hallucination would currently overstate the evidence.
A better supported statement is that AI assisted trial matching is already being used to improve access and therefore hallucination in this workflow could plausibly affect both patient inclusion and trial accrual quality. In oncology even small matching errors can matter because the patient pool is limited and eligibility windows are narrow.
Example: cancer trial matching
Imagine a patient with metastatic breast cancer and incomplete biomarker documentation. An AI tool scans the chart and suggests a clinical trial match. It may infer that the patient has already received a required line of therapy or that a biomarker is positive based on ambiguous text. If that output is wrong the patient may be excluded from review or routed to an inappropriate study.
Recommended by LinkedIn
The harm is twofold. First the patient may lose access to a potentially valuable trial. Second the team may waste time validating a false lead while missing a legitimate opportunity elsewhere. In a fragmented record environment this risk is higher because hallucinations are more likely when the model has to infer missing details.
Detecting hallucinations in Pharma drug delivery
In Pharma drug delivery and medical operations hallucination detection should be built into the workflow rather than treated as a retrospective audit. The most effective strategies are:
In drug delivery operations this is especially important when AI is used for patient support, medication, counselling, adherence, messaging or protocol based communication. Errors here can lead to wrong timing, wrong context or incorrect interpretation of product instructions.
What good governance looks like
Healthcare and Pharma organisations should treat hallucination as a safety metric not a branding issue. That means tracking hallucination rate by use case severity and downstream harm potential. It also means not deploying general purpose LLMs into high risk workflow steps unless there is source grounding auditability and human oversight.
The most mature approach is layered. AI can screen summarise and suggest but humans must decide. Model outputs should be versioned, logged and continuously monitored. And when hallucinations are detected they should feed back into prompt design workflow redesign and governance review.
Ethical guidelines for LLM use
Ethically LLMs should support clinical judgment rather than replace it. The literature stresses that users must be informed about the probabilistic nature of model outputs and the possibility of hallucination. Transparency, informed use and explicit human accountability are central ethical requirements.
In practice that means:
Case studies of hallucination incidents
Documented incidents show why these safeguards matter. In one widely cited medical example ChatGPT produced fabricated references when prompted about homocystinuria associated osteoporosis and late onset Pompe disease. In another study LLMs repeated planted errors in vignettes in up to 83% of cases showing that hallucination can amplify existing misinformation rather than simply inventing new errors.
A more operational case comes from clinical trial matching. AI systems have been shown to misclassify metastatic status or overstate eligibility in oncology screening workflows which can lead to false positives and false negatives. In one study physician review rejected 17.10% of AI generated trial notifications because they were false positives. That illustrates a major point: even when AI improves speed it still needs human control to prevent downstream harm.
AI hallucination is not just a technical nuisance
In clinical and Pharma practice it can become a hidden system for error amplification exclusion and misdirection. The most serious risk may not be the obvious wrong answer. It may be the confident falsehood that quietly shapes who gets reviewed, what gets prioritised and which patients never reach the trial table.
For oncology trial matching drug delivery and healthcare operations the path forward is not to abandon AI. It is to constrain it with evidence grounded workflows, human oversight and safety metrics that measure not only correctness but harm. Confidence is useful only when it is earned.
By: Khizra Ahmad
Founder & CEO HealthMAIT
Health and Medical AI Training Ltd.
References
Regulators don't care about your model's accuracy score. They care about who signed off on the data integrity.
Great insights Khizra Ahmad, MSc, MA, PGDip. Thanks for sharing ✨️
Excellent message Khizra Ahmad, MSc, MA, PGDip.
"Almost right is not good enough" is something we are starting to see surface in how medical education talks about AI tools. Hallucinations as a systems issue rather than just a model problem is the framing that changes what we actually need to teach.
Important topic Khizra Ahmad, MSc, MA, PGDip. In high-stakes fields like healthcare, the issue isn’t just what AI can do, it’s how rigorously we validate what it produces. When “almost right” isn’t acceptable, governance and human oversight become non-negotiable.