Clinical decision support powered by large language models (LLMs) is moving from experimentation to evaluation, bringing both opportunity and risk into focus. Work in gastroenterology demonstrates how sociodemographic details embedded in patient presentations can steer LLM recommendations in different directions even when clinical symptoms are identical. Purpose-built synthetic case vignettes, validated for plausibility and relevance, enable systematic testing that moves beyond isolated examples.
The emerging picture, however, underscores practical limits. The way a prompt is phrased can shift answers, and assessing only outputs may be insufficient for safe deployment. In parallel, interpretability efforts seek to illuminate how models process information, offering a route to greater transparency. Together these strands suggest a needed pivot from tallying answers to scrutinising behaviour.
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Synthetic Vignettes Enable Structured Bias Detection
A structured experimental approach begins with synthetic clinical vignettes designed to test whether sociodemographic attributes influence LLM outputs. One hundred distinct gastroenterology scenarios were created and vetted by two senior gastroenterologists to ensure clinical plausibility and relevance. The prompt design then altered only sociodemographic attributes while holding clinical content constant. This strategy isolates the effect of identity markers from clinical factors, allowing direct attribution of any output shifts to patient demographics rather than differences in presentation.
Each vignette was instantiated in 34 versions, comprising neutral control and 33 sociodemographic variations. Ten LLMs answered four clinical questions for every version. The resulting 136000 responses provide a large, systematically generated dataset that captures how recommendations change across controlled identity dimensions. The accumulated evidence shows that LLM responses can be biased by sociodemographic information. In an illustrative case of identical abdominal pain symptoms, the presence of demographic indicators associated with low socioeconomic status aligns with an increased likelihood of mental health screening recommendations, whereas the absence of such indicators aligns with standard gastroenterological workup. By design, the vignette framework attributes these divergent paths to identity signals rather than clinical difference.
This methodology also addresses concerns about synthetic data. Validation by experienced clinicians during construction anchors each scenario to clinical reality. That grounding helps convert synthetic material into a dependable substrate for probing response patterns, while the controlled manipulations make findings interpretable. The approach therefore supplies a reproducible testbed for characterising output behaviour across diverse presentations and identity markers.
Scale and Variability Expose Stable Patterns and Practical Gaps
The experimental scale is central to extracting stable patterns from models known to produce variable outputs. LLMs can hallucinate, introducing unpredictable deviations that typically mask consistent tendencies. By collecting 136000 responses across many presentations, identity variants, models and questions, the analysis aggregates enough evidence to surface regularities that would otherwise be lost in noise. The multidimensional design broadens coverage of clinical contexts and sociodemographic cohorts, supporting precise characterisation of how recommendations vary by identity features.
Yet scale does not remove all sources of variability with clinical implications. Prompt sensitivity remains a salient limitation for practice. Evidence indicates that small changes in the arrangement of words can alter outputs even when the underlying facts do not change. Because the experimentation relied on uniform prompts, it did not explore how different but clinically reasonable phrasings might shift recommendations for the same case. In real workflows, clinicians vary how they frame questions. If an LLM delivers different guidance for equivalent inputs phrased differently, decision support could become inconsistent day to day or clinician to clinician.
This gap matters for deployment. Without testing prompt sensitivity, it is difficult to specify reliable operating procedures for model use. Two identical patient presentations could receive conflicting recommendations from the same model simply because of variations in query structure. The framework therefore documents bias patterns under controlled prompting but stops short of resolving how those patterns behave under the linguistic variability of practice. Recognising this boundary avoids overextending conclusions beyond tested conditions.
Interpretability Paths Highlight Opportunities and Uncertainties
Understanding outputs alone may be insufficient to assure safety. Complementary progress in interpretability seeks to illuminate internal mechanisms, shifting attention from what models say to how they process information. One line of work constructs simplified representations of complex LLMs by replacing abstract computations with interpretable features that correspond to clinical concepts, such as the presence of dyspnoea. Researchers then identify patterns in how these features interact, aggregate them to reconstruct reasoning pathways and visualise computational graphs that trace input to output.
If such approaches mature, they could enable clinicians to observe how clinical information is transformed and organised inside model architectures, see how knowledge is synthesised into diagnostic suggestions and identify which factors most influence final recommendations. These capabilities would help distinguish responses grounded in coherent clinical reasoning from those driven by superficial statistical associations, a distinction that underpins professional trust in decision support.
However, the practical implications remain untested in clinical settings. Current tools operate on proxy models rather than clinical LLMs, raising questions about the validity of insights derived from simplified surrogates. The absence of clinical evaluation warrants caution and may prompt scepticism among practitioners. Still, active research into transparency provides grounds for measured optimism. Integrating rigorous pattern documentation from vignette-based characterisation with interpretability methods could bridge the gap between detecting bias and understanding its origins. Such a synthesis might convert limitations into pathways for improvement, guiding model development toward behaviours that are observable and verifiable.
Systematic characterisation of clinical LLMs using validated synthetic vignettes shows that sociodemographic information can bias recommendations while demonstrating a scalable path to document behavioural patterns across models and scenarios. At the same time, prompt sensitivity and the limits of output-only analysis constrain immediate clinical utility. Progress is likely to depend on coupling robust experimental frameworks with interpretability techniques that expose how models organise and weigh clinical information. For healthcare professionals, the practical takeaway is twofold: rigorous testing can reveal consistent patterns despite variability, and transparency into internal processing may be necessary to translate those findings into reliable decision support. Together these directions outline a cautious but constructive route toward trustworthy clinical deployment.
Source: JAMA Network Open
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