Large language models (LLMs) have emerged as transformative tools in healthcare, offering advancements in medical research, diagnostics and decision-making. However, their reliance on vast amounts of data sourced from the internet exposes them to significant risks, particularly data-poisoning attacks. Malicious actors can deliberately introduce false medical information into publicly available datasets, which are subsequently used for model training. Even minimal amounts of misinformation can lead to models that propagate medical errors while maintaining high scores on standard evaluation benchmarks. Understanding the vulnerabilities of medical LLMs, assessing the impact of data poisoning and exploring effective mitigation strategies are essential to ensuring their reliability and safety in healthcare.
The Risk of Data Poisoning in Medical LLMs
Medical LLMs are trained on diverse sources, including peer-reviewed databases such as PubMed and unverified web-scraped datasets like the Common Crawl. While curated sources undergo human moderation, many training datasets lack similar oversight, making them vulnerable to poisoning. Attackers can introduce misinformation into these datasets by uploading fabricated web pages, modifying existing content or embedding misleading information into seemingly legitimate sources. Studies have demonstrated that replacing just 0.001% of training tokens with medical misinformation can substantially increase the likelihood of an LLM generating incorrect or harmful medical advice.
Must Read: Harnessing LLMs for Medical Data Extraction
This issue is exacerbated by the persistence of misinformation on the internet. Once uploaded, misleading content can remain accessible indefinitely, ensuring that even future iterations of LLMs are at risk of ingesting and propagating erroneous medical knowledge. Moreover, widely used benchmarks for evaluating medical LLMs fail to detect poisoned models. Existing tests, such as MedQA and PubMedQA, focus on assessing knowledge retrieval and reasoning but do not explicitly measure the presence of misinformation. Consequently, a poisoned model may continue to perform well on standard benchmarks while still producing medically harmful outputs.
The Impact of Data Poisoning on Model Performance
Despite their advanced capabilities, medical LLMs lack intrinsic mechanisms for distinguishing between accurate and false information. Experiments have shown that models trained on datasets containing even a small fraction of poisoned data are more likely to generate misleading statements on treatments, medication and diseases. Alarmingly, these models maintain comparable performance scores to their unpoisoned counterparts when evaluated using traditional NLP benchmarks. This suggests that conventional assessment methods are insufficient for detecting the effects of data poisoning.
A key concern is that incorrect medical advice generated by LLMs could negatively influence clinical decision-making, ultimately affecting patient care. If left unchecked, poisoned models could reinforce misinformation, making it harder for medical professionals and researchers to trust AI-generated insights. Furthermore, existing mitigation strategies, including reinforcement learning with human feedback and retrieval-augmented generation, have shown limited effectiveness in filtering out false information once a model has been trained. The difficulty in retrospectively identifying and removing misinformation highlights the need for proactive strategies to prevent contamination during training.
Mitigation Strategies Using Knowledge Graphs
Given the limitations of current evaluation methods, researchers have explored the use of biomedical knowledge graphs to validate LLM-generated outputs. These knowledge graphs provide structured repositories of medical facts, mapping relationships between diseases, treatments and medications. By cross-referencing LLM-generated content against an authoritative knowledge graph, potential misinformation can be identified and flagged before it is disseminated.
This method has proven highly effective in detecting harmful content, capturing over 90% of misinformation in medical LLM outputs. Unlike traditional filtering techniques, which rely on adjusting training data or fine-tuning models, knowledge graph-based validation functions independently of the training process. This allows for real-time verification of AI-generated medical content, ensuring that information remains aligned with established medical knowledge. The approach is also scalable and does not require extensive computational resources, making it a viable solution for enhancing the safety and reliability of medical LLMs in clinical and research environments.
The integration of LLMs into healthcare presents both opportunities and risks. While these models offer significant potential in medical research and decision-making, their vulnerability to data-poisoning attacks underscores the need for stronger safeguards. The persistence of misinformation in web-scraped datasets makes it imperative to develop rigorous validation techniques beyond conventional benchmarking. Biomedical knowledge graphs provide a promising solution by enabling independent verification of LLM-generated content, reducing the risk of misinformation. As AI continues to shape the future of healthcare, proactive measures must be adopted to ensure that medical LLMs remain accurate, trustworthy and safe for clinical use.
Source: nature medicine
Image Credit: iStock
