Controlling AI hallucinations: Building evidence-based trust in clinical and scientific workflows
Generative artificial intelligence (GenAI) holds immense potential to revolutionise complex, regulated industries, yet, recent data reveals a sobering reality that leaders can no longer ignore. In a 2025 cross-industry survey, 44% of organisations reported experiencing negative consequences from generative AI use, with average financial losses of $4.4 million per incident.
In the pharmaceutical and healthcare sectors, where precision is paramount and human lives are directly impacted, these consequences manifest most dangerously as AI hallucinations, fabricated or inaccurate outputs presented with unwarranted confidence. As the integration of this powerful technology accelerates across critical workflows, including medical writing, clinical research, and literature review, unreliable AI has emerged as the central barrier to clinical trust.
To harness the true capabilities of generative AI safely, the industry must transition from utilising generic open-ended models to demanding evidence-grounded systems that prioritise verifiable clinical data and rigorous human oversight.
The escalating risk of hallucinations in healthcare
The consequences of AI hallucinations are already measurable and severe, demonstrating that these inaccuracies are enterprise-level liabilities, not rare edge cases. In healthcare and pharmaceutical environments, even minor deviations from factual accuracy can scale rapidly into significant patient safety hazards, regulatory findings, litigation, and reputational damage. When incorrect or misleading information infiltrates AI-assisted documentation, it can easily propagate across electronic health records, ultimately influencing clinical decisions, delaying appropriate care, and compromising patient outcomes.
Evidence indicates that current medical AI systems remain highly vulnerable to these fundamental flaws. A 2023 JAMA Network Open Paper about AI-generated discharge summaries demonstrated that 18% of cases contained incomplete or misleading information. Similarly, in high-stakes professional environments, professionals have faced severe repercussions for submitting reports featuring phantom footnotes and invented data generated by artificial intelligence.
General-purpose AI tools were fundamentally not designed for evidence-based medical environments, rendering them inherently unreliable for workflows that demand strict traceability, citation accuracy, and regulatory compliance. The problem is further compounded by current user behaviours and systemic platform limitations. Research reveals that only 39% of individuals verify AI-generated information using external sources, highlighting a dangerous gap between reliance on automation and essential validation practices. Furthermore, hallucinations are significantly exacerbated when individuals apply artificial intelligence to massive, poorly structured datasets using vague prompts. This approach often exceeds the underlying model’s context window, confusing the system and triggering fabricated responses disguised as authoritative facts.
Advancing document-grounded generative models
To mitigate these profound risks, pharmaceutical organisations and clinical research teams must fundamentally redesign how they integrate artificial intelligence into their daily operations. Hallucinations are not an unavoidable flaw of large language models. They can be reduced through thoughtful deployment, careful prompt design, and curated, well-structured input data, with expert review serving as a critical safeguard before outputs influence regulatory submissions and medical communications. When hallucinations are reduced, medical writing teams can place greater trust in AI outputs, using manual verification to confirm accuracy, rather than fixing fundamentally unreliable drafts.
The strategic opportunity lies in shifting from broad, open-ended applications to highly structured, document-grounded approaches. For example, when drafting a clinical study report section, the system restricts its outputs to the sponsor’s trial protocols, statistical data, and approved source documents, and surfaces citations for each paragraph. By anchoring AI outputs strictly to verified source materials, medical writing teams can drastically reduce the likelihood of fabricated data in complex tasks such as writing clinical study reports, comprehensive literature reviews, and regulatory submissions.
This targeted methodology transforms artificial intelligence from an unpredictable text generator into a highly dependable tool that supports faster synthesis of evidence and reduced rework for medical writers. Implementing this operational paradigm requires a deliberate focus on system architecture and robust governance. Institutions must adopt specific, evidence-first tactics to ensure reliability and secure the trust of medical practitioners, regulatory bodies, and patients alike.
- Implement document-grounded reasoning: Organisations must configure AI systems to prioritise verified, uploaded medical literature, approved labelling, and peer-reviewed search results above all else. By constraining the model to extract answers exclusively from governed libraries and high-quality sources, rather than relying on its broad historical training data, organisations can virtually eliminate the primary catalyst for fabricated information. Systems should be designed to display the exact source documents and provide the specific statements used to support each conclusion.
- Enforce conservative information handling: Systems must be deliberately designed to recognise their limitations. When clinical trial data or patient information is missing or incomplete, artificial intelligence should be programmed to acknowledge the data gap explicitly or provide qualitative assessments. It must be restricted from attempting to guess or invent numerical values simply to satisfy a user’s prompt.
- Mandate expert human oversight: AI must be unequivocally positioned as a supportive tool, not an autonomous replacement for clinical, scientific expertise or judgement. Workflows must require rigorous human review, ensuring that pharmacists, physicians, researchers, medical writers, pharmacovigilance scientists, regulatory affairs professionals, and treating physicians retain full accountability for validating AI-generated interpretations against the original clinical or scientific evidence.
The shift towards implementation discipline
The broader healthcare industry shift is moving rapidly from technological adoption to stringent AI implementation discipline. Moving forward, trust in artificial intelligence within the pharmaceutical sector will depend significantly less on the raw computational capabilities of a given model and far more on how these systems are architected, validated, and embedded into highly regulated environments.
As developers and healthcare institutions collaborate to enforce evidence-first configurations and transparent sourcing, the pervasive risk of hallucinations will steadily diminish. For clinical and pharmaceutical industry professionals navigating this unprecedented transformation, these structural advancements offer a clear, actionable pathway to leveraging AI platforms safely. By prioritising verifiable data and human accountability, organisations can ultimately improve operational efficiency without ever sacrificing the rigorous accuracy required in regulated industries and patient care.
About the author
Ome Ogbru, PharmD, is the CEO and founder of AINGENS, a life sciences software company building evidence-first AI platforms for scientific and medical workflows. With over 20 years of experience across pharma, biotech, and healthcare, his background includes roles as a clinical pharmacist, professor, and global medical information leader, where he worked at the intersection of science, regulation, and content creation.
