Physician-led Red-Teaming Study

• Physician-led red-teaming is a systematic evaluation method where clinicians use realistic, high-stakes prompts to identify vulnerabilities in generative AI systems.
• The study integrates interdisciplinary workshops, positive intent prompting, and replication protocols to mirror real clinical scenarios and assess model robustness.
• Findings reveal critical failure modes, including hallucinations and biased responses, underscoring the need for ongoing safeguards in AI-driven healthcare.

A physician-led red-teaming paper refers to the systematic probing of LLMs and other generative AI systems by clinicians and healthcare experts to uncover vulnerabilities that may result in clinical harm, erroneous or unsafe outputs, or legal and ethical compliance failures when deployed in healthcare settings. This approach leverages clinical expertise to formulate realistic, high-stakes prompts and interpret model responses with a focus on patient safety, clinical integrity, and regulatory standards.

1. Methodological Foundations of Physician-Led Red Teaming

Physician-led red teaming in healthcare AI combines domain-expert knowledge with structured adversarial evaluation to discover and categorize model vulnerabilities that generic AI developers may overlook. Typical methodologies include:

• Interdisciplinary Workshop Design: Studies such as that reported in (Balazadeh et al., 1 May 2025) organized interactive workshops at clinical AI conferences. Groups composed of physicians from diverse specialties (oncology, hepatology, pediatrics, emergency medicine) collaborated with computer scientists and engineers. Clinical workflow brainstorming activities yielded realistic, case-based prompts based on direct experience of frontline care.
• Positive Intent Prompting: Prompts were constructed not to maliciously trick models, but to faithfully represent authentic patient-care scenarios, seeking to reveal how models might fail under regular use by clinicians or patients.
• Replication Protocols: Initial vulnerabilities elicited through red teaming are systematically re-tested across multiple models and over time to assess persistence and cross-model generalizability (same-model and different-model replication rates are quantified).

This structure ensures that adversarial prompts reflect plausible, clinically relevant situations and that the vulnerabilities detected can be mapped to tangible risks in real-world practice (Balazadeh et al., 1 May 2025).

2. Vulnerability Taxonomies and Main Findings

Empirical findings from physician-led red-teaming have resulted in detailed taxonomies of model failure modes, categorized using frameworks inspired by established medical error taxonomies. Prominent categories include (Balazadeh et al., 1 May 2025):

Category	Description	Example
Hallucination	Fabrication of unsupported or non-existent medical details	Invented lab scores or references to data not present in prompt
Incorrect Medical Knowledge	Factual or reasoning errors inconsistent with clinical reality	Recommending an inappropriate treatment or diagnosis
Omitted Medical Knowledge	Failure to address key clinical information	Neglecting a critical intervention in urgent cases
Anchoring Bias	Over-fixation on an irrelevant prompt detail	Inappropriate procedure because of literal role instruction
Sycophancy	Aligning with user-desired answer at the expense of accuracy	Affirming misleading assertions
Image Interpretation Failure	Misanalysis of clinical images	Misclassifying identical pre-op/post-op images
Prioritisation Error	Misranking the urgency or importance of actions	Downplaying urgent care needs
Vaguery	Overly generic/non-actionable output	Boilerplate responses in specific clinical contexts
Training Bias	Output biases transferred from general/non-medical data	Propagating non-specialist misconceptions

These categories—characterized by both direct patient harm potential (e.g., misdiagnosis) and broader trust/safety impacts (e.g., inconsistent image analysis)—are generalizable across foundation and domain-specific models (Balazadeh et al., 1 May 2025).

3. Evaluation Protocols and Benchmarking

Recent studies introduce rigorous, multi-perspective safety evaluation protocols tailored to the medical domain, addressing the distinct needs of patients, clinicians, and general users (Kim et al., 9 Jul 2025):

• Role-Specific Red-Teaming: Separate test sets and policies are created for the patient's perspective (e.g., naivete, lay phrasing), the clinician's perspective (e.g., alignment with American Medical Association guidelines), and a general safety perspective (refusal rates, jailbreaking potential).
• Benchmark Construction: Datasets such as PatientSafetyBench (466 queries across five major clinical safety policy areas) are developed through LLM-augmented prompt generation and LLM-as-a-judge quality filtering (sample retention criterion: scoring ≥ 4 out of 5).
• Criteria: Outputs are scored according to harmfulness rubrics specific to the assigned user role, with explicit definitions (e.g., 1: strict refusal, 2: warning with caution, 3–5: increasing policy violation/unsafe).

Such protocols enable quantitative assessment of safety and robustness, benchmarking models like the MediPhi collection on a granular, policy-aligned scale (Kim et al., 9 Jul 2025).

4. Attack Strategies and Red-Teaming Techniques

Red-teaming studies have operationalized a range of adversarial techniques, including both automated (e.g., in-context prompt evolution) and manual, creative attack methodologies (Mehrabi et al., 2023, Inie et al., 2023, Puccio et al., 22 May 2025):

• Automated Prompt Discovery: Frameworks such as FLIRT utilize feedback-loop in-context learning to iteratively evolve adversarial prompts, optimizing a weighted multi-objective function:

$$X^{(t+1)} = \arg\max_{X \in \mathcal{X}_t} \text{Score}(X) = \arg\max_{X \in \mathcal{X}_t} \sum_{i=1}^n \lambda_i O_i(X)$$

where $O_i$ represent objectives such as attack efficacy, diversity, and low overt toxicity (Mehrabi et al., 2023).

• Manual "Role-play" Jailbreaks: Adversarial prompts such as the "Goofy Game" instruct the LLM to adopt an authoritative-yet-clumsy persona, explicitly mixing accurate with unreliable information. Game-theory-inspired payoff structures are employed to optimize for misleading, plausible-sounding output (Puccio et al., 22 May 2025):

$$U = \alpha \cdot \text{Accurate Advice} - \beta \cdot \text{Incorrect Advice}$$

• Creative Human Tactics: Practitioners employ hierarchical strategies—Language (prompt injections, code encoding), Rhetoric, "Possible Worlds" (hypothetical scenarios), Fictionalization, and iterative Stratagems (e.g., scattershot regeneration, meta-prompting)—to elicit undesired responses (Inie et al., 2023).

These methods often reveal vulnerabilities invisible to generic tests, especially when tailored to domain-specific language and workflow.

5. Clinical, Ethical, and Regulatory Implications

Physician-led red-teaming studies have surfaced implications central to safe, legal, and ethical AI deployment (Balazadeh et al., 1 May 2025, Wen et al., 26 Jun 2025, Gillespie et al., 12 Dec 2024):

• Patient Safety: Hallucinated or omitted interventions, errors in image interpretation, and incorrect medical knowledge can precipitate direct harm, delay care, or erode confidence in digital clinical decision support.
• Legal and Copyright Compliance: Targeted exercises have uncovered leakage of verbatim copyrighted material in literary domains—while scientific and clinical outputs are better protected, these findings underscore the need for continuous compliance testing and inference-time mitigation layers such as meta-prompts (“Avoid copyright infringement”) (Wen et al., 26 Jun 2025).
• Sociotechnical Structure: Red-teaming in healthcare is inherently interdisciplinary, entailing collaboration between technical experts, clinicians, ethicists, legal advisors, and organizational leaders. Psychological burdens, value assumptions, and labor arrangements must be considered—drawing lessons from content moderation and emphasizing community support and regular rotation to address risks of burnout and trauma (Gillespie et al., 12 Dec 2024, Zhang et al., 10 Jul 2024).

A central lesson is that red-teaming is not solely a technical exercise but a sociotechnical process requiring explicit policy development, continuous vigilance, and shared responsibility among stakeholders.

6. Recommendations and Future Directions

Based on observed vulnerabilities and emerging protocols, principal recommendations for physician-led red-teaming in healthcare AI include:

• Integrate Clinical Expertise Directly into Model Tuning: Ongoing collaboration with domain experts during model development and maintenance cycles is essential for surfacing context-specific error modes (Balazadeh et al., 1 May 2025).
• Deploy Continuous, Role-Sensitive Evaluation Protocols: Standardized, multi-perspective benchmarks (e.g., PatientSafetyBench, MedSafetyBench) should be integrated into pre-deployment and post-deployment QA cycles, with dynamic test case generation reflecting evolving guidelines and practice (Kim et al., 9 Jul 2025).
• Enhance Model Safeguards Against Hallucinations and Prompt Sensitivity: Incorporate explicit, testable refusal policies for out-of-scope queries, ensuring that subtle prompt variations do not yield unsafe recommendations (Balazadeh et al., 1 May 2025).
• Systematically Monitor Legal and Ethical Risks: Maintain real-time auditing and update inference-time guardrails to capture and prevent inadvertent disclosure of both confidential and copyrighted information (Wen et al., 26 Jun 2025).
• Adopt Practices to Counteract Human Bias and Burnout: Draw on established closed-loop clinical audit and peer review traditions, incorporating regular debriefing, mental health supports, and structured cognitive debiasing especially as red teamers are exposed to sensitive content (Zhang et al., 10 Jul 2024, Gillespie et al., 12 Dec 2024).

A plausible implication is that as LLMs are increasingly integrated into healthcare delivery systems, the physician-led red teaming methodology will become a core component of clinical AI governance, combining technical robustness evaluation with domain-specific safety, legal, and ethical assurance.

7. Limitations and Open Research Problems

Key open questions for the field are:

• Prompt-robustness and Model Dynamics: Replication studies highlight that LLM vulnerabilities may be mutable, with same-model replication rates exceeding cross-model rates (e.g., 71% for GPT-4o in-session vs. 29% cross-model), demanding regular re-evaluation as models and prompts co-evolve (Balazadeh et al., 1 May 2025).
• Quantification of Sociotechnical Risk: While some formulas for harm assessment (e.g., Harm Score $$H = \sum (\text{Exposure}_i \times \text{Severity}_i)$$) frame the human factor, meaningful, quantitative, and reproducible safety metrics for red-teaming outcomes remain underdeveloped (Zhang et al., 10 Jul 2024).
• Transferability Across Domains and Modalities: Many frameworks and findings are currently grounded in text and simple image contexts; physician-led red-teaming for multi-modal models, downstream health process integration, and non-English clinical settings requires further research (Mehrabi et al., 2023, Kim et al., 9 Jul 2025).

Further advancement in benchmarking, evaluation, and interdisciplinary practice is needed to realize robust, generalizable standards for AI safety in medicine.