Title: CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision-Language Models

Benchmark: CARES
Summary: CARES is a benchmark for evaluating the "trustworthiness" of medical vision-language models (Med-LVLMs). It assesses five dimensions: trustfulness, fairness, safety, privacy, and robustness. Some of these are via the QA benchmark, others are via supplemental testing eg. attempted jailbreaks. 41K QA pairs from 18K images across 16 medical modalities and 27 anatomical regions.

Phenomenon type: Specific Application (A single use case, where the benchmark is likely to be examples of that use case)
Target phenomenon: Trustworthiness, robustness

Phenomenon defined by authors: Yes
Phenomenon definition: Medical trustworthiness defined to include five criteria: factuality, fairness, safety, privacy, and robustness. Each is measured with task-specific setups and distinct metrics, but "factuality" - performance on QA - is the most important part of the benchmark.

Definition scope: Comprehensive
Definition integrity: Composite phenomenon
Definition integrity detail: Yes
Phenomenon contested: Contested
Task definition: QA on medical questions (closed or open-ended), classify images, or provide reports to assess five trust dimensions.

Task item definition: A task item includes an image and/or text and a question (yes/no, multi-choice, or open-ended)
Task definition detail: Some tasks include uncertainty prompts or jailbreak prompts to test robustness/safety.

Task source: Modified from another benchmark (e.g. translation into another language), LLM-generated task examples (e.g. Filtered from responses to a prompt)
Task ecology: Partial real task (e.g. answering medical questions collected from real people)
Task ecology detail: Tasks simulate medical image interpretation and advice, aligning with clinical support use cases.

Dataset size: 41000
Dataset metadata available: Yes
Dataset metadata detail: Age, Gender, Race, Imaging Modality, Anatomical Region, Question Type

Sampling method: Targeted items (creators defined a task space and chose tasks within it strategically), Specific criteria (items were taken from a larger set based on specified rules)
Split reviewed: Test
Response format: Multiple choice, Free response (e.g. summary paragraph, executable code), Structured response (e.g. valid JSON, API call alone)
Response format detail: Open-ended scored with LLMs, others use accuracy or abstention (also LLM-counted)

Metric definition: Exact Match (accuracy, F1, precision, recall), LLM-as-a-Judge (text quality, preferences, NOT extracting answers for other metrics)
Metric definition detail: GPT-4 scoring for open-ended questions, abstention rates, overconfidence; Perspective API for toxicity

Metric aggregation: Simple Mean
Metric statistics: Inter-rater agreement (Krippendorff’s alpha), statistical comparisons

Metric subscores: Yes
Metric subscores detail: By task and dimension (e.g., fairness by age, race, gender)

Metric metascoring: consensus@k (plurality vote over k trials)
Metric access: Outputs alone
Few-shot evaluation: Yes
Authorship: Academia
Benchmark availability: Yes
Task face validity: Yes
Metric face validity: Yes
Results interpreted by authors: Yes
Results compared: Yes
Results comparison explanation: Yes
Results realism: No
Human baseline: No
Authors discuss validity: Yes
Author validity detail: Compared models’ performance with known biases (e.g., age), and used expert-based evaluation protocols.

Taxonomy: Medicine / 
New taxonomy: Specific Application / Medicine