Clinician-Guided Benchmark Profiling

• Clinician-guided benchmark profiling is a systematic evaluation method that aligns clinical decision workflows with expert-curated benchmarks.
• It employs realistic clinical queries, unified preprocessing, and multi-dimensional metrics to ensure reliability and translational accuracy.
• This approach enhances reproducibility and trust in AI-driven clinical decision support across varied domains such as EHR analysis, diagnostic reasoning, and imaging interpretation.

Clinician-guided benchmark profiling refers to the systematic evaluation and comparison of computational models, algorithms, or information retrieval systems in clinical medicine using testbeds, datasets, or evaluation protocols that are specifically aligned with the information needs, reasoning demands, and expert practices of healthcare professionals. This concept is central to accelerating innovation, reproducibility, and practical impact in clinical decision support systems (CDSS), LLMs for electronic health records (EHR), AI-assisted clinical trial design, mental health assessment, and medical imaging interpretation. By integrating real-world clinician questions, domain-specific annotation, and expert-driven rubrics, clinician-guided benchmarking enables robust and actionable assessment of system capabilities beyond standard leaderboard metrics.

1. Historical Context and Motivation

The need for clinician-guided benchmarks emerged from the observation that standard computational evaluation methods—such as matching textual queries to pre-defined answers or using generic datasets—were insufficient for capturing the full complexity of clinical decision-making. For instance, the TREC Clinical Decision Support (CDS) track (Nguyen et al., 2018) was launched to support evidence-based medicine by tasking systems with retrieving biomedical literature in response to actual clinical questions sourced from simulated or real EHRs. Over three years, 87 teams participated with 395 submitted runs, collectively exploring a variety of search techniques, but heterogeneity in platforms, preprocessing, and undocumented parameters reduced result comparability and diminished the ability to build on previous work.

Clinician-guided benchmarking directly addresses these limitations by anchoring evaluation protocols in authentic data and workflows encountered by clinicians. In domains such as psychiatric care (Liu et al., 28 Feb 2025), mental health assessment via social media (Roy et al., 2023), and clinical trial design (Neehal et al., 25 Jun 2024), researchers now develop datasets, evaluation frameworks, and error taxonomies that tightly correspond to clinical tasks—thereby promoting both technical rigor and translational utility.

2. Methodological Principles

The key principles underlying clinician-guided benchmark profiling are:

• Stable, Unified Platforms: Systems such as the platform described in (Nguyen et al., 2018) use a fixed corpus (e.g., 1.25 million full-text PubMed Central articles indexed with Apache Solr 6.0.1) and unified query/document preprocessing pipelines (negation detection, concept extraction, demographic normalization).
• Realistic Queries and Tasks: Benchmarks draw clinical queries from actual EHRs, physician practices, or curated instruction sets (Fleming et al., 2023).
• Expert-driven Annotation: Gold standard responses, rubrics, or feature lists are created by clinicians (e.g., 303 clinician-written reference answers in MedAlign).
• Multi-dimensional Metrics: Evaluation spans traditional retrieval or generation metrics (infNDCG, infAP, F1 score, BLEU, accuracy) and clinical dimensions such as reasoning complexity, factual consistency, and clinical relevance.
• Reproducibility and Statistical Rigor: Experiments are run under controlled conditions with paired statistical tests (e.g., two-sample t-tests at 95% or 98% confidence in (Nguyen et al., 2018)), enhancing confidence in findings.

3. Benchmark Construction and Data Curation

Several recent frameworks exemplify rigorous clinician-guided benchmarking:

Benchmark	Domain	Clinician Involvement
TREC CDS	IR/biomedical	Clinical question curation, query design (Nguyen et al., 2018)
MedAlign	EHR/text gen	15 clinicians; reference answers and evaluation protocol (Fleming et al., 2023)
PsychBench	Psychiatry	Multi-center real patient records; 60-clinician reader paper (Liu et al., 28 Feb 2025)
CTBench	Clinical trials	Expert validation of baseline feature matching; web interface for annotation (Neehal et al., 25 Jun 2024)
CliMedBench	Chinese medical LLM	14 scenarios, expert Who–What–How taxonomy; human evaluation alongside automatic metrics (Ouyang et al., 4 Oct 2024)
BioMed-VITAL	Multimodal vision	Expert demonstration selection, preference annotation, reference-aligned selection (Cui et al., 19 Jun 2024)

In all cases, the benchmarks are grounded in authentic clinical workflow, spanning queries about diagnostic reasoning, treatment recommendation, EHR summarization, eligibility and baseline feature selection for clinical trials, or psychiatric differential analysis.

4. Evaluation Protocols and Statistical Analysis

Clinician-guided benchmarking protocols deploy task-specific scoring schemes and robust statistical tests to enable objective comparison:

• Retrieval Effectiveness: infNDCG, infAP, R-Precision, P@10 for IR tasks (Nguyen et al., 2018).
• Model Agreement: Agreement rates with clinicians (e.g., 70% PK-iL vs. 47% for baseline XAI) and inter-rater reliability (0.72) (Roy et al., 2023).
• Robustness Testing: Fragility and robustness scores under stress-testing (e.g., R(m) = (1/5) ∑ᵢ₌₁⁵ (1 – fᵢ(m)), where fᵢ(m) measures performance drop in stress scenario Tᵢ) as detailed in (Gu et al., 22 Sep 2025).
• Human-in-the-Loop Validation: Cohen’s Kappa > 0.78 for LM evaluator agreement with clinicians (Neehal et al., 25 Jun 2024).
• Prompt and Reasoning Calibration: Quantitative impact of prompt engineering and chain-of-thought on LLM metrics (e.g., CoT sometimes lowering medication match scores in psychiatric tasks (Liu et al., 28 Feb 2025)).
• Item Response Theory (IRT): Use of the three-parameter logistic model in adaptive CAT evaluation, P(X₍ᵢⱼ₎ = 1 | θⱼ) = cᵢ + (1 - cᵢ) / [1 + e^–aᵢ (θⱼ – bᵢ)], for rapid, ability-differentiated testing (Ouyang et al., 4 Oct 2024).

Such protocols allow for fine-grained diagnostic insight, not just gross accuracy measures.

5. Comparative Insights and Failure Modes

Clinician-guided profiling has exposed key strengths and limitations of leading computational models:

• Shortcut Learning and Brittleness: As demonstrated in (Gu et al., 22 Sep 2025), leading models (e.g., GPT-5) can maintain high accuracy by exploiting superficial cues (format, answer position) or guessing correctly when images are removed, while suffering significant drops under perturbations (accuracy 80.89% with images vs. 67.56% without). Format perturbation (answer order shuffling) revealed reliance on positional heuristics.
• Clinical Reasoning Gaps: Domain-specialized LLMs can lag behind general models on reasoning and factual consistency (Ouyang et al., 4 Oct 2024); open-ended tasks, nuanced reasoning, and complex decision-making remain challenging.
• Context Length Constraints: Accuracy drops by ~8.3% for GPT-4 when EHR context is truncated from 32k to 2k tokens (Fleming et al., 2023); input window limitations impede practical utility for long clinical records.
• Interpretability and Trust: PK-iL yields actionable, clinician-friendly explanations directly tracing predictions to process knowledge conditions (e.g., mapping Reddit posts to PHQ-9 or CSSRS criteria), improving agreement and trust (Roy et al., 2023).
• Subgroup Support: Reader studies find that junior clinicians benefit most from LLM decision support in psychiatric care, with measurable efficiency and diagnostic improvements (Liu et al., 28 Feb 2025).

6. Guidelines and Future Directions

Recommendations for advancing clinician-guided benchmark profiling include:

• Standardization and Transparency: Open access platforms for reproducible evaluation (e.g., MedAlign, CTBench, BioMed-VITAL datasets available online).
• Rubric Design: Development of clinician-guided rubrics that decompose benchmarks along reasoning complexity, clinical context, and input modality.
• Metric Diversification: Disaggregated reporting of performance across stress scenarios and clinically meaningful dimensions (robustness, factuality, comprehensiveness, generalizability).
• Adversarial and Robustness Testing: Routine integration of perturbation, ablation, and adversarial test sets to expose failure modes not visible in standard benchmarking.
• Continuous Feedback: Iterative refinement of benchmarks and evaluation processes through clinician feedback, error analysis, and adaptive testing protocols (e.g., IRT-based CAT).

These practices ensure that computational methods do not simply optimize for leaderboard scores, but genuinely augment clinical reasoning and decision support.

7. Significance and Impact

Clinician-guided benchmark profiling underpins the rigorous advancement of evidence-based, trustworthy, and robust AI systems in clinical medicine. By aligning evaluation with authentic clinical scenarios, incorporating expert judgment, and targeting multi-faceted metrics (robustness, interpretability, factual consistency), it enables researchers and practitioners to distinguish true progress from superficial gains. The shift from accuracy-centric leaderboards to clinically meaningful, stress-tested, and context-sensitive assessment is essential for the safe, effective, and reliable deployment of computational models in healthcare practice.