Bias in Medical AI: The Three Biases Product Teams Keep Underestimating

Medical AI systems are often evaluated through a narrow lens: accuracy, performance benchmarks, or dataset size. Yet many of the most consequential failures in healthcare-adjacent AI do not arise from incorrect predictions, but from how systems are designed, framed, and interacted with over time. Bias, in this sense, is not only a data problem. It is a system problem.

Recent literature has begun to formalise this broader view. Hanna et al. (2025) distinguish three forms of bias that frequently appear in medical AI systems: representation bias, interaction bias, and feedback loop bias. While widely acknowledged in theory, these biases are often underestimated or addressed too late in practice, particularly at the product and system-design level.

This article briefly examines each bias and outlines why architectural choices, rather than model improvements alone, are central to mitigation.

1. Representation Bias: When Data Coverage Is Mistaken for Neutrality

Representation bias arises when the data used to train or inform an AI system fails to adequately reflect the diversity of the population it serves. In healthcare contexts, this problem is well documented: imbalances in age, ethnicity, socioeconomic background, and health-seeking behaviour are common across clinical datasets (Hanna et al., 2025).

Foundation models amplify this challenge. Although large language models are trained on vast corpora, their data sources, selection criteria, and filtering processes remain largely opaque. Independent evaluations suggest uneven compliance with emerging regulatory frameworks such as the EU AI Act, reinforcing concerns about hidden representational gaps (Bommasani et al., 2023).

Crucially, most application developers cannot correct representation bias at the model level. However, system-level design choices can reduce its downstream impact. Hybrid neuro-symbolic architectures, combining statistical pattern recognition with structured symbolic reasoning, offer one pathway. Symbolic layers grounded in verified medical knowledge, such as clinical taxonomies and knowledge graphs, can constrain generative outputs in sensitive contexts, prioritising consistency and traceability over plausibility (Boden, 2016; Chaudhri et al., 2022; Velasquez et al., 2025).

In this sense, representational bias is not eliminated, but contained through architectural restraint.

2. Interaction Bias: When Confidence Becomes a Design Flaw

Interaction bias emerges not from what a system knows, but from how users interpret and rely on its responses. In healthcare-adjacent settings, conversational interfaces can inadvertently encourage over-trust, especially when responses are fluent, confident, and emotionally reassuring.

Empirical research suggests that users often disclose more information to AI systems than to clinicians, perceiving them as less judgemental and more accessible (Adamopoulou and Moussiades, 2020). While this can support early reflection and documentation, it also increases the risk that users treat AI outputs as authoritative rather than assistive.

Product teams frequently underestimate this form of bias because it is not visible in traditional evaluation metrics. A system can be technically accurate yet still mislead through tone, framing, or implied certainty.

Mitigation here depends less on additional data and more on interaction design. Techniques include avoiding deterministic or diagnostic language, explicitly communicating system limitations, framing outputs as decision support rather than recommendations, and embedding escalation pathways when uncertainty or risk increases.

These measures align with broader calls for uncertainty-aware AI design, particularly in domains where human judgement remains essential (Bengio et al., 2024).

3. Feedback Loop Bias: When Systems Learn the Wrong Lessons

Feedback loop bias occurs when repeated interactions subtly reshape system behaviour in ways that reinforce narrow perspectives or inappropriate patterns. In medical contexts, this can happen when systems adapt continuously to user input without sufficient safeguards, gradually amplifying specific concerns, behaviours, or interpretations.

Interactive Machine Learning frameworks emphasise the importance of Human-in-the-Loop control, where learning is deliberate rather than automatic (Mosqueira-Rey et al., 2022). However, many deployed systems blur this distinction, updating implicitly based on usage patterns rather than explicit feedback.

One approach to limiting feedback loop bias is to decouple routine interaction from system learning. Systems may log interactions for transparency and review, but update their behaviour only when users provide explicit, informed feedback. Additional safeguards, such as screening emotionally charged inputs or anomalous usage patterns, can further reduce unintended adaptation.

These strategies reflect a shift from optimisation-driven design toward governance-aware system evolution.

Bias as a System Property, Not a Model Defect

Across all three categories, a common theme emerges: bias in medical AI is rarely confined to datasets or algorithms alone. It is produced and mitigated through design decisions spanning architecture, interaction, governance, and feedback handling.

Frameworks such as the Artificial Intelligence Management Strategies proposed by O'Hara and Hall (2025) provide a useful lens for categorising these interventions, from paternalistic harm-reduction measures to more open, collaborative controls. Yet no single strategy is sufficient. As AI systems become more embedded in sensitive domains, layered safeguards become a necessity rather than an optional feature.

Recognising bias as a system-level phenomenon shifts responsibility. It places ethical and practical obligations not only on model providers, but on application designers, product teams, and organisations deploying AI in real-world healthcare settings.

In medical AI, accuracy matters. Architecture, interaction, and restraint matter just as much.

References

Hanna, M.G., Pantanowitz, L., Jackson, B., Palmer, O., Visweswaran, S., Pantanowitz, J., Deebajah, M. and Rashidi, H.H. (2025) Ethical and bias considerations in artificial intelligence/machine learning. Modern Pathology, 38(3), p. 100686. https://doi.org/10.1016/j.modpat.2024.100686

Bommasani, R., Klyman, K., Zhang, D. and Liang, P. (2023) Do foundation model providers comply with the draft EU AI Act? Stanford Center for Research on Foundation Models (CRFM). https://crfm.stanford.edu/2023/06/15/eu-ai-act.html

Boden, M.A. (2016) AI: Its nature and future. Oxford: Oxford University Press.

Chaudhri, V.K., Baru, C., Chittar, N., Dong, X.L., Genesereth, M., Hendler, J., Kalyanpur, A., Lenat, D.B., Sequeda, J., Vrandečić, D. and Wang, K. (2022) Knowledge graphs: Introduction, history, and perspectives. AI Magazine, 43(1), pp. 17–29. https://doi.org/10.1002/aaai.12033

Velasquez, A., Bhatt, N., Topcu, U., Wang, Z., Sycara, K., Stepputtis, S., Neema, S. and Vallabha, G. (2025) Neurosymbolic AI as an antithesis to scaling laws. PNAS Nexus, 4(5), pgaf117. https://doi.org/10.1093/pnasnexus/pgaf117

Adamopoulou, E. and Moussiades, L. (2020) An overview of chatbot technology. Artificial Intelligence Applications and Innovations, IFIP Advances in Information and Communication Technology, vol. 583. https://doi.org/10.1007/978-3-030-49186-4_31

Bengio, Y., Bowman, D., Christiano, P., Clark, J., Dafoe, A., Dario, S., et al. (2024) Preparing for the possibility of a radically empowered AI. Science, 384(6695), pp. 275–284. https://doi.org/10.1126/science.adk1246

Mosqueira-Rey, E., Moreira, I., Mures, D., Gómez, A. and Naya, F. (2022) Human-in-the-loop machine learning: A survey and perspective of interactive and teacher-guided machine learning. Information Fusion, 82, pp. 99–123. https://doi.org/10.1016/j.inffus.2022.01.003

O'Hara, K. and Hall, W. (2025) Five AI management strategies and how they could shape the future. Atlantic Council. https://www.atlanticcouncil.org/blogs/new-atlanticist/five-ai-management-strategies-and-how-they-could-shape-the-future/