LLMs are artificial-intelligence systems trained on enormous collections of text and other data to predict and generate sequences of language. In a clinical environment, that ability can be used to summarize patient records, draft medical notes, explain complex information, retrieve relevant research and assist with diagnostic reasoning. Some models are also being connected to imaging systems, laboratory data and electronic health records. This integration gives them access to highly sensitive information and places their outputs closer to real medical decisions. The review notes that many healthcare workers are already using such tools informally, sometimes without institutional approval, technical safeguards or clear instructions about responsibility. This unofficial “shadow use” makes it difficult for hospitals to know which systems are being used, what information is being entered and whether the resulting recommendations influence patient care.

The authors emphasize that risk is not confined to the moment when a clinician receives an AI-generated answer. It can emerge throughout the entire lifecycle of a system, beginning with model design and continuing through data collection, training, deployment, software updates and everyday use. A model may inherit weaknesses from its training data, including incomplete representation of populations, outdated medical knowledge or systematic biases. Data used to train or adapt a model can also be deliberately manipulated in an attack known as data poisoning. By inserting carefully crafted examples into a training set, an attacker may influence how the model behaves later. Because modern LLMs are complex and difficult to interpret, identifying the precise cause of a dangerous response can be challenging even after an incident has occurred.

Another cybersecurity threat involves prompt injection. In this type of attack, hidden or misleading instructions are embedded in text supplied to the model. The instructions can redirect the model away from its intended task, cause it to disclose information or make it ignore clinically relevant evidence. In a medical setting, a compromised prompt might cause an AI system to overlook an abnormality in a pathology report or fail to identify a tumor that is visible in a tissue sample. Prompt injection can be especially difficult to prevent when an LLM processes material from multiple sources, such as clinician instructions, patient records, web pages and laboratory reports. If the system treats all text as equally authoritative, malicious content may be mistaken for a legitimate command. Beyond the model itself, weaknesses in hospital networks, application programming interfaces and cloud infrastructure could expose patient data or interrupt essential clinical services.

The most familiar model-inherent hazard is the phenomenon commonly called hallucination: the generation of information that sounds convincing but is false, unsupported or invented. LLMs do not retrieve truth in the human sense. They generate statistically plausible sequences based on patterns learned during training, unless they are connected to carefully controlled databases or retrieval systems. Even retrieval-augmented systems can produce errors if the source material is incomplete, poorly indexed or misinterpreted. In medicine, a fabricated citation, incorrect drug interaction or inaccurate diagnostic explanation could have immediate consequences. The danger is amplified by the fluent and confident style of many models, which can make a wrong answer appear more reliable than a cautious human response.

The review also examines how human behavior can turn technical imperfections into clinical harm. Clinicians may develop automation bias, placing too much trust in an AI recommendation simply because it was produced by a sophisticated system. This can reduce the likelihood that an output will be independently checked, particularly in busy environments where staff face time pressure and heavy workloads. The opposite problem can occur when an AI system is used to confirm an existing belief. If a clinician suspects a particular diagnosis, a model may be prompted in a way that encourages it to support that assumption, reinforcing confirmation bias rather than challenging it. LLMs can also adapt their language to the apparent expectations of the user, producing answers that are more agreeable than accurate. Long or complicated conversations create further risks because errors can accumulate across multiple exchanges and become difficult to trace.

Privacy and accountability add another layer of complexity. Many commercially available AI services are hosted outside the healthcare institution, meaning that hospitals may have limited control over where patient information is processed, how long it is retained and whether it is used for further system development. Even when providers promise data protection, healthcare organizations must understand the technical and legal arrangements governing access, storage and deletion. De-identification can reduce privacy risks, but it is not a universal solution: detailed clinical narratives may contain enough unusual information to make individuals identifiable when combined with other data. At the same time, responsibility can become blurred when a recommendation is produced by a model, reviewed by a clinician and embedded in software supplied by a third party. The authors argue that clinical accountability must remain explicit rather than being dispersed across an opaque technical chain.

To reduce these dangers, the researchers call for security to be built into AI development from the beginning rather than added after deployment. This includes carefully curating training and fine-tuning data, testing models against adversarial attacks, documenting known limitations and evaluating performance on clinically representative cases. Hospitals should monitor systems continuously instead of treating approval as a one-time event. Monitoring can include tracking error patterns, unexpected changes in performance, suspicious user activity and the effects of software updates. The authors recommend clear institutional rules defining which tools may be used, what data may be entered, when human review is mandatory and who is responsible for responding to incidents. They also propose local teams dedicated to supervising AI in clinical practice, supported by centralized AI Security Operations Centers, or SOCs, that could identify threats across institutions and coordinate responses to attacks or failures.

The review further argues that regulation must evolve alongside rapidly changing AI technology. Only a small proportion of AI systems are formally approved as medical devices, while many general-purpose models can influence healthcare without fitting neatly into existing regulatory categories. Traditional frameworks were largely designed for products that remain stable after approval. LLMs, by contrast, may change through updated training data, modified instructions, new connected tools or silent software revisions. A system that performs well during an initial evaluation may behave differently months later or when used in a new hospital with a different patient population. The authors therefore support regulatory approaches based on continuous evaluation, post-deployment surveillance and transparent reporting of significant changes. Stephen Gilbert, Professor of Medical Device Regulatory Science at TU Dresden, says that oversight and technological development must be more closely integrated if patients and health systems are to benefit safely from these tools.

The researchers stress that safer medical AI will require cooperation among engineers, clinicians, cybersecurity specialists, regulators, ethicists and patients. Human oversight remains essential, but oversight must be meaningful: clinicians need sufficient training, enough time to review AI outputs and access to information about how systems were evaluated. Institutions should also make responsible use easier than unofficial use by providing approved tools, practical guidance and channels for reporting errors without fear of punishment. “AI is already being used in healthcare, often without formal oversight,” says Jakob N. Kather, Professor of Clinical AI at the Else Kröner Fresenius Center. “The key question is how to implement these systems in a way that is transparent, robust, and aligned with clinical responsibility.” The review’s central message is not that LLMs should be kept out of medicine, but that their deployment must be treated as an ongoing safety process. Powerful language models may become valuable clinical partners, but only if hospitals continuously test, monitor and govern them with the same seriousness applied to other technologies that can affect human life.

Subject of Research: Safety and security risks of large language models in healthcare

Article Title: Safety and security of large language models in healthcare

News Publication Date: 19 August 2026

Web References: https://doi.org/10.1038/s41586-026-10687-1

References: Clusmann J, Freyer O, Ostermann M, Ferber D, Ghaffari Laleh N, Hilgers L, Kolbinger FR, Schneider CV, Downing A, Wekenborg MK, Gilbert S, Foersch S, Truhn D, Wiest IC, Kather JN. “Safety and security of large language models in healthcare.” Nature. 2026. DOI: 10.1038/s41586-026-10687-1

Keywords: Large language models; artificial intelligence; healthcare; clinical AI; patient safety; cybersecurity; data privacy; prompt injection; data poisoning; hallucinations; automation bias; AI regulation; medical devices; AI governance

A notable meeting held on March 28, organized by a coalition of stroke physicians, researchers, and industry leaders, focused on the transformative impact AI has already begun to exert in the realm of stroke treatment. Spearheaded by Dr. Joseph Broderick from the University of Cincinnati, the discussions culminated in an insightful article published in the journal Stroke on September 30, reflecting on the state of AI in stroke and its future trajectory.

Current applications of AI in stroke medicine predominantly revolve around augmenting clinical decision-making capabilities, particularly concerning the analysis of brain and vascular imaging. Moreover, AI systems are proving invaluable in identifying potential candidates for clinical trials, thereby streamlining the recruitment process and enabling more thorough research. Despite these advancements, there is a pressing need for the design of “human in the loop” systems, which necessitate human oversight and expertise in both training and utilizing AI algorithms.

Dr. Broderick likened the learning process of AI to that of a toddler learning to ride a bicycle. The analogy underscores the importance of human guidance—just as children benefit from training wheels and expert support during their learning phases, AI systems require similar human intervention to mitigate errors and improve outcomes. This is particularly significant when considering the stakes involved in medical AI applications, where inadequate training and oversight can lead to substantial misjudgments and potentially life-threatening consequences.

The current state of machine learning (ML) and generative AI reveals critical differences that illustrate the strengths and weaknesses of each approach in stroke applications. Machine learning necessitates training AI models on structured, meticulously curated datasets, a process that, while demanding, yields interpretable results that can fundamentally enhance clinical decision-making. The transparency of ML models engenders trust and promotes adherence to medical validation protocols that are essential in healthcare.

Conversely, generative AI models, trained on extensive and often untested bodies of input data, introduce their own set of complexities. Although they benefit from reduced human intervention during training, they rely on substantial computational resources, raising concerns about energy consumption and environmental sustainability. A significant drawback is their ‘black box’ nature, which complicates the assessment of how specific decisions or recommendations are arrived at, thereby raising challenges surrounding accountability and medical plausibility.

Ensuring data robustness remains paramount for stroke researchers. Efforts must be made to compile comprehensive datasets that encompass data from diverse scanner manufacturers, healthcare institutions, and patient populations. Were these models trained on flawed or insufficient data—without the corrective touch of human experts—AI could yield detrimental recommendations, presenting a valid cause for concern among clinicians.

The protection of patient privacy adds another layer of complexity to the integration of AI into healthcare. Stringent safeguards and protocols must be implemented to ensure that patient data utilized during AI training adheres to privacy standards and complies with regulations, such as HIPAA. Strategies might include employing independent third-party organizations—like the American Heart Association—to aggregate anonymized datasets for AI model training, thus streamlining data sharing while upholding patient confidentiality.

Another avenue for the evolving role of AI is the potential to enhance patient recruitment for clinical trials. Robust AI models could dramatically improve the efficiency of identifying suitable participants, facilitating better communication of trial details to patients in accessible language, and even transcending language barriers through translation services. Beyond recruitment, AI also holds promise for personalizing treatment approaches, tailoring interventions to individual patient profiles based on data-driven insights.

In conjunction with the focus on AI, the conversation at the roundtable extended to innovative clinical trial designs, emphasizing the need for platform trials that test multiple hypotheses simultaneously and the incorporation of pragmatic trials that evaluate treatment efficacy in real-world settings. These methodologies stand to significantly mitigate costs and streamline logistical burdens associated with traditional trial frameworks.

Emphasizing community engagement is a vital component for the advancement of stroke research. Integrating feedback from frontline medical staff—including emergency medical technicians and trial coordinators—is crucial for refining trial designs and reducing the burden on both participants and investigators. Establishing common objectives for clinical trials can enhance participation rates and hasten the dissemination of findings to regulatory bodies and the public at large.

Looking ahead, the role of AI within stroke treatment appears exceptionally promising. As researchers explore avenues for maximizing the benefits of AI and other novel tools in clinical research, they are cognizant of the larger societal implications. The challenge lies not only in adopting these advanced technological solutions but also in discerning accurate data from potentially misleading information in an era inundated with digital noise. Dr. Broderick poignantly remarks that while AI is akin to fire—capable of both constructive and destructive outcomes—its responsible utilization remains a collective responsibility that must be urgently addressed.

The synthesis of AI in stroke treatment embodies an exciting frontier wherein medical science and engineering converge to improve patient outcomes significantly. By recognizing the challenges, such as data integrity and privacy concerns, medical researchers can harness the power of AI judiciously to propel stroke care into a new era, unlocking unprecedented possibilities for precision medicine and data-driven clinical insights.

Ultimately, the journey towards integrating AI into stroke care marks a significant milestone in the fusion of technology and medicine, promising to deliver enhanced tools for clinical practice while ensuring ethical stewardship over patient data and well-being.

Subject of Research: The use of AI in stroke clinical trial design and treatment.
Article Title: Artificial Intelligence and Novel Trial Designs for Acute Ischemic Stroke: Opportunities and Challenges
News Publication Date: 30-Sep-2025
Web References: Stroke Journal Article
References: Article from Stroke Journal, published by the University of Cincinnati.
Image Credits: Photo/University of Cincinnati

Keywords

Artificial intelligence, Neurology, Stroke research, Clinical trials, Patient privacy, Machine learning, Generative AI, Healthcare technology.

Medical LLMs are sophisticated neural networks that continuously learn from clinical knowledge, patient histories, and medical literature. Their ability to understand and generate human-like text has allowed these models to assist clinicians by synthesizing information, proposing diagnostic hypotheses, and even drafting patient communications. Yet, this promising potential is shadowed by the vulnerability of these models to malicious manipulation. The study led by Yang et al. meticulously dissects how adversarial prompts—carefully crafted inputs designed to mislead the model—and fine-tuning attacks—where an attacker subtly modifies the model’s parameters—can lead medical LLMs to produce dangerously inaccurate or harmful outputs.

The implications of such vulnerabilities are profound. In healthcare, trustworthiness is paramount; an AI model that can be easily duped or corrupted threatens clinical decisions, patient safety, and ethical standards. Unlike generic language models, medical LLMs operate in a domain where errors can be fatal. The study reveals that adversarial prompt attacks can force models to override safety guardrails deliberately embedded into their design. For instance, they may be coerced into recommending contraindicated medications or insufficient treatment protocols, demonstrating how adversarial tactics exploit inherent model weaknesses.

Through meticulous experimentation, the researchers showed that adversarial prompts were capable of altering the model’s behavior in ways that subtly but significantly manipulated clinical recommendations. This undermining of internal safety constraints indicates that conventional prompt-based AI usage, often lauded for its flexibility, can become a vector for harm when deployed in sensitive environments like healthcare. Equally alarming is the susceptibility of medical LLMs to fine-tuning attacks, wherein attackers inject malicious updates into the model’s training process. Such interventions can permanently skew the model’s outputs, creating hidden backdoors that evade detection during routine usage.

The methodology employed in the study draws from adversarial machine learning—a field that investigates how AI systems can be tricked or misled by hostile actors. The authors skillfully combined prompt engineering techniques with sophisticated model manipulation to simulate real-world attack scenarios. These ranged from simple textual inputs intended to provoke incorrect responses to complex re-training strategies designed to inject malevolent knowledge covertly. By aggressively targeting both the input-output interface and the model’s internal architecture, the research paints a comprehensive portrait of AI vulnerabilities that have, until now, been underappreciated in healthcare AI research.

Further complicating matters, the study illuminates that these adversarial methods can be performed without access to the original training data or proprietary model internals, dramatically lowering the bar for attackers. This democratization of security risks presents a formidable challenge for developers and clinicians who rely on medical LLMs. With adversarial prompt attacks achievable through User inputs and fine-tuning attacks potentially executable during model version updates or via compromised cloud infrastructure, safeguarding the integrity of these systems emerges as an urgent imperative.

In response to their findings, the authors advocate for a multi-pronged defense strategy to protect medical LLMs from adversarial threats. This includes the design of robust input preprocessing filters to detect and neutralize suspicious prompts, the implementation of verification protocols during model fine-tuning to detect unauthorized parameter changes, and the employment of ensemble modeling to cross-validate outputs. They additionally stress the importance of transparency and auditability in AI systems, envisioning mechanisms whereby clinicians can trace how and why a given model output was generated, thereby increasing accountability and trust.

Moreover, the study highlights the vital need for regulatory frameworks that specifically address AI vulnerabilities in healthcare. Existing regulations often overlook adversarial risks, focusing instead on data privacy and compliance standards. Yang et al. urge policymakers to consider AI robustness as a central pillar of future healthcare AI deployments, ensuring that systems undergo rigorous adversarial testing before clinical integration. The authors propose that collaboration between AI researchers, clinical practitioners, and cybersecurity experts is essential for establishing standards that safeguard patient welfare against adversarial manipulation.

The challenges outlined in this research underscore a broader conundrum for AI in medicine: achieving the delicate balance between model complexity and security. Medical LLMs rely on vast and intricate architectures to process ever-growing datasets, but this intricacy exponentially increases the surfaces vulnerable to attack. While improving model capabilities remains the frontier of research, parallel investments in security fortifications become non-negotiable. This reveals a paradigm shift in AI development culture, where security considerations must be embedded from inception rather than retrofitted as afterthoughts.

To illustrate the gravity of these adversarial attacks, the study presents case studies where incorrect medical advice derived from malicious prompts could lead to severe patient outcomes. These range from erroneous drug prescriptions potentially causing adverse drug reactions to misdiagnosed conditions delaying critical interventions. Such scenarios transcend theoretical risks, marking a clarion call for the medical AI community to pivot towards comprehensive safety-first approaches in model design, deployment, and maintenance.

Interestingly, the findings also emphasize the resilience of certain model architectures compared to others, hinting at future research directions focused on building inherently robust medical LLMs. The heterogeneous performance responses to attacks across different models suggest that selecting architectures and training protocols with security in mind can mitigate some risks. The authors stress that no single solution exists; rather, a layered defense with diverse strategies is essential to outpace adversarial ingenuity.

The study’s revelations arrive at a critical time when healthcare systems worldwide are progressively adopting AI technologies to tackle rising patient loads and complex clinical dilemmas. Deploying medical LLMs without addressing these new security vulnerabilities could jeopardize not only patient health but also public trust in AI innovations. The meticulous work by Yang and colleagues provides a roadmap for the AI community to rethink security paradigms, promoting safer medical AI deployment while preserving the transformative benefits of large language models.

In conclusion, while medical large language models herald a new epoch of AI-assisted healthcare, their vulnerabilities to adversarial prompt and fine-tuning attacks expose a stealthy and significant threat. Harnessing the power of these models responsibly requires that researchers, clinicians, and policymakers collectively prioritize robustness against malicious manipulation. As the AI healthcare ecosystem matures, integrating adversarial resistance into the foundational fabric of medical LLMs will be crucial to safeguard patient well-being and unlock the true potential of AI-driven medicine.

Subject of Research: Adversarial attacks and security vulnerabilities in medical large language models (LLMs)

Article Title: Adversarial prompt and fine-tuning attacks threaten medical large language models.

Article References:
Yang, Y., Jin, Q., Huang, F. et al. Adversarial prompt and fine-tuning attacks threaten medical large language models.
Nat Commun 16, 9011 (2025). https://doi.org/10.1038/s41467-025-64062-1

Image Credits: AI Generated