Biomedical foundation models are increasingly being integrated into healthcare, but their evaluation methods often fail to reflect real-world conditions. Performance can deteriorate when data sources, clinical workflows or user interactions shift, creating risks for patient safety and clinical decision-making. An analysis of more than 50 biomedical models revealed that almost one third included no robustness testing and many relied only on cross-dataset consistency, which offers limited insight when the links between datasets are unclear. Few assessments considered shifted, synthetic or external data despite their relevance to deployment. A structured approach that aligns testing with intended tasks and environments is needed to provide reliable benchmarks, strengthen trust and translate regulatory objectives into practical measures.
Why Robustness Lags Behind Capability
Foundation models differ from earlier predictive systems because users can steer behaviour at inference through in-context learning, instruction following, tool use and prompting. These capabilities blur boundaries between development and deployment, multiplying opportunities for exploitation and failure under distribution shifts. Natural shifts arise from evolving disease presentation or population structure while adversarial shifts include prompt injection, jailbreaks, data poisoning or backdoor triggers that alter behaviour at training or inference. Both yield out-of-distribution inputs that can be domain specific and difficult to trace. Traditional robustness frameworks offer only partial coverage: adversarial approaches search within distance bounds that may not reflect real clinical artefacts, and interventional approaches require causal graphs that are not always available. As a result, nominal guarantees can fail to translate into robust behaviour in specialised context-rich healthcare settings.
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The evidence base remains thin. Among more than 50 biomedical foundation models examined, 31.4% reported no robustness assessment, 33.3% used consistency across multiple datasets as the primary signal and only small fractions evaluated on shifted data, synthetic data or external sites. Such patterns limit confidence when models meet new scanners, acquisition protocols, paraphrased prompts or incomplete clinical context. The burden falls on deployment teams to anticipate failure modes that the original evaluations never exercised.
Priority-Based Specifications for Testing
A pragmatic path is to specify robustness by deployment priorities rather than by abstract threat models alone. Two elements shape a useful specification: the degradation mechanisms most likely to occur in context and the performance metric that must be protected. Instead of seeking exhaustive coverage, priority-based testing concentrates effort where clinical risk is highest and where perturbations reflect realistic artefacts. This perspective still overlaps with threat-bounded tests but aligns evaluation with meaningful scenarios and output quality for the task at hand.
Operationalising the idea requires breaking robustness into units that become quantitative tests with guarantees. Knowledge integrity is one example: foundation models can be misled by typos, distracting biomedical entities or misinformation inserted into prompts while medical image models are sensitive to common artefacts, organ morphology or orientation. Testing should therefore privilege realistic text transforms and imaging artefacts over arbitrary string or pixel perturbations. Population structure is another axis: group robustness examines performance gaps across identifiable or latent subpopulations while instance robustness targets corner cases where every prediction must meet a minimum threshold. Uncertainty awareness completes the set, separating aleatoric variation from epistemic gaps and checking whether the model handles paraphrasing, missing context or clearly out-of-scope inputs with appropriate behaviour.
Concrete specifications make these categories actionable. For an over-the-counter pharmacy chatbot, priorities include multi-turn dialogue on common medications, handling partial patient information, dosage limits, adverse interactions, refusals for non-OTC requests, paraphrases, typos and off-topic inputs. For an MRI report copilot, priorities include multi-turn review across several images, variation across leading scanner vendors, contrast and resolution changes, common artefacts with refusals when image quality is too low, anatomical knowledge including spatial relations and rejection of non-MRI inputs. Each priority can be translated into small targeted tests that reflect real use.
From Policy to Practice
As models evolve into compound systems—multi-expert or multiagent architectures coordinated to handle diverse clinical tasks—robustness must be specified at both component and system levels. Each subsystem warrants tests aligned with its role with attention to bottlenecks and cascading effects when one element fails. Evaluation should also consider trade-offs among metrics and stakeholder perspectives. For example, summarisation or report generation may shift clinical emphasis in ways that influence downstream decisions so behavioural robustness with clinicians in the loop becomes part of the testing remit.
Policy frameworks are advancing but leave implementation gaps. Leading AI regimes acknowledge natural and adversarial robustness yet stop short of domain-specific requirements while health IT rules emphasise transparency over detailed robustness standards. Mandating task- and domain-based specifications can bridge this divide by mapping policy objectives to executable tests, informing quantitative risk thresholds or safety cases and accommodating differences in permissible tasks and user groups. Community-endorsed specifications would help developers choose architectures and training methods that balance robustness with accuracy, guide deployment teams in selecting prompt templates and calibrating user confidence and provide templates for failure reporting and incident management. By integrating specifications with feedback loops and user training, organisations can identify vulnerabilities quickly, adjust prompts or workflows and pursue targeted updates to improve reliability over time.
Robustness for biomedical foundation models cannot be an afterthought or a generic checklist. Evidence shows that current practice often omits meaningful tests or leans on proxies that fail under the pressures of real clinical context. Specifying robustness by priority grounded in realistic degradation mechanisms and task-critical metrics offers a scalable way to standardise evaluations, align with stakeholder risk and translate regulatory aims into practice. By adopting community-endorsed specifications that span knowledge integrity, population structure, uncertainty management and compound system behaviour, healthcare organisations can close the gap between laboratory performance and dependable deployment, improving safety while enabling responsible automation at scale.
Source: npj digital medicine
Image Credit: iStock
