Imported from mdbabumiamssm/AI-Agentic-Skills-by-Dr.-Mia (Skills/Clinical/ClinicalLlmEvaluationBenchmarking_Agent/SKILL.md). Install upstream with npx skills add mdbabumiamssm/AI-Agentic-Skills-by-Dr.-Mia --skill ClinicalLlmEvaluationBenchmarking_Agent. Copyright stays with the author.

name: 'clinical-llm-evaluation-benchmarking' description: 'Design and run rigorous clinical LLM evaluation workflows grounded in systematic review evidence on AI-generated clinical note quality methods.' measurable_outcome: 'Execute skill workflow successfully with valid output within 15 minutes.' allowed-tools:

• read_file
• run_shell_command
• web_fetch

Clinical LLM Evaluation Benchmarking

Overview

This skill guides the design, execution, and interpretation of evaluations for clinical LLM outputs, with emphasis on AI-generated clinical notes and adjacent clinical text workflows. It helps select appropriate metrics, clinician review procedures, safety labels, omission and harm scoring, dataset provenance checks, and regression gates before any clinical deployment or workflow expansion.

Use this skill to avoid relying on a single automatic score when clinical usefulness, factual correctness, patient safety, and documentation risk all need to be evaluated together.

When to Use This Skill

• Evaluating AI-generated clinical notes, summaries, discharge instructions, consult drafts, radiology-style text, or handoff documentation.
• Comparing multiple LLMs, prompts, retrieval pipelines, note templates, or deployment configurations for clinical use.
• Designing a benchmark dataset with known provenance, consent constraints, de-identification status, and clinical specialty coverage.
• Creating safety labels for hallucination, omission, contradiction, bias, inappropriate advice, unsafe escalation, or guideline discordance.
• Setting clinician adjudication workflows for factuality, completeness, usefulness, readability, and potential patient harm.
• Building regression gates before model, prompt, retrieval corpus, or EHR integration changes.
• Auditing whether automatic metrics align with expert clinical judgment for a specific clinical task.

Core Capabilities

1. Evaluation scope definition
   Identify the clinical task, intended users, deployment context, acceptable use boundaries, and failure modes before choosing metrics.

2. Dataset provenance and governance review
   Record source systems, cohort criteria, date ranges, de-identification method, annotation status, specialty mix, inclusion and exclusion criteria, and patient privacy constraints.

3. Fine-grained medical Q&A dataset evaluation
   For domain-specific medical Q&A benchmarks, apply an item-level error taxonomy; check ambiguity, answerability, answer validity, clinical risk, and domain coverage; evaluate clinically relevant subgroup slices; verify answer alignment with supporting evidence; define and report reliability thresholds; and document dataset provenance, annotation decisions, omissions, hallucinations, leakage checks, prompt sensitivity, limitations, and trustworthy-use boundaries.

4. Systematic clinical-note evaluation methods
   For AI-generated clinical note benchmarks, combine factual consistency checks, omission detection, risk-of-harm labels, note completeness scoring, and inter-rater reliability reporting; design benchmark datasets so automated evaluation methods can be experimentally compared with clinician scoring rather than assumed equivalent.

5. Clinical-note quality measure taxonomy and validity
   Organize clinical-note quality measures into factuality, completeness, structure, readability, harmfulness, and clinician preference; report automated metrics separately from blinded human review, and assess benchmark validity by matching each measure to its intended quality dimension and experimentally checking whether automated results align with blinded clinical judgments.

6. Metric selection
   Match the evaluation target to metric families such as factual consistency, clinical completeness, omission detection, guideline concordance, readability, usefulness, calibration, and safety risk. Treat lexical similarity and embedding similarity as supporting signals rather than standalone clinical quality measures.

7. Clinician adjudication design
   Define reviewer qualifications, rating rubrics, blinded review procedures, disagreement resolution, inter-rater agreement reporting, escalation criteria, and examples for each label.

8. Omission and harm scoring
   Score missing critical information separately from incorrect additions, and classify likely harm severity using task-specific clinical risk categories.

9. Safety and bias labeling
   Label hallucinations, unsupported claims, contradictions, unsafe triage, medication errors, delayed-care risk, inappropriate certainty, protected-class bias, and patient comprehension risks.

10. Experimental benchmark execution
    Compare candidate systems under controlled prompts, fixed input sets, reproducible model settings, documented retrieval sources, and consistent post-processing.

11. Regression gates and release criteria
    Convert evaluation results into pass/fail thresholds for deployment, including minimum clinician approval, maximum critical-error rate, no unresolved severe harm findings, and no regression on sentinel cases.

12. Result interpretation
    Summarize quantitative scores with confidence intervals where appropriate, describe representative failures, separate clinical risk from style preference, and document limits of generalization.

13. Evaluation artifact packaging
    Produce a reusable benchmark plan, annotation guide, data card, metric table, adjudication summary, risk register, regression checklist, and deployment recommendation.

14. On-premises diagnosis deployment evaluation
    For open-source distilled reasoning LLMs proposed for on-premises clinical diagnosis, design controlled evaluations of diagnostic accuracy, calibration, uncertainty reporting, quantization and distillation degradation, local hardware constraints, privacy-constrained deployment checks, latency, and failure modes; select clinically relevant baselines, including proprietary-model comparators when appropriate; define deployment-specific safety gates and task-specific go/no-go thresholds versus those baselines; and require safety review, governance approval, and regression testing before clinical use.

15. Specialty-stratified cross-provider ICU nursing QA benchmarking
    Construct specialty-specific intensive care nursing question sets and run blinded comparisons across providers such as ChatGPT, DeepSeek, and Google Gemini using expert-reference scoring; apply appropriate statistical model comparisons, report uncertainty, evaluate factual accuracy, clinical relevance, and clinically important omissions separately, and define escalation requirements for uncertain, unsafe, or high-risk answers. Treat exam-question performance as task-specific evidence and do not generalize it to bedside safety without prospective clinical workflow validation.

16. Clinical response correctness and harm evaluation
    Evaluate correctness, clinically important omission, and risk of harm as separate dimensions using clinician adjudication, severity-weighted scoring, and confidence intervals; report explicit examples of unsafe answers rather than relying on aggregate accuracy alone.

17. Fine-grained domain Q&A adjudication
    Evaluate domain-specific clinical Q&A at the item level using clinically meaningful error taxonomies, separate omission and harm labels, clinically relevant subgroup analysis, evidence-grounding checks, and calibration assessment; adjudicate individual answers and failure modes rather than relying on aggregate accuracy alone.

18. Trustworthy domain-specific Q&A dataset auditing
    Audit each benchmark item for ambiguity, evidence sufficiency, omissions, harmfulness, subgroup difficulty, and potential contamination; report these findings transparently alongside benchmark limitations and trustworthy-use boundaries.

19. Fine-grained domain Q&A audit controls
    Audit domain-specific Q&A datasets with an item taxonomy, ambiguity and answerability checks, clinician adjudication, separate omission and harmfulness labels, subgroup analysis, and controls against benchmark contamination.

20. Blinded specialist adjudication of correctness, omission, and harm
    Evaluate correctness, clinically important omission, and risk of harm as separate endpoints; require blinded specialist adjudication with a prespecified process for resolving disagreements; weight errors by clinical severity; and separately analyze unsafe answers stated with confidence.

21. Atomic scoring for fine-grained domain Q&A evaluation Score domain-specific Q&A outputs at the atomic item or answer-component level for correctness, completeness, evidence support, uncertainty, harmful omissions, and subgroup performance; audit dataset quality alongside model performance, and use aggregate accuracy only as a secondary summary rather than the sole evaluation criterion.

22. Systematic clinical-note quality measurement and reporting Separate clinical-note quality measures into factuality, omission, structure, usability, safety, and downstream task utility; report evaluator reliability, assess agreement between human judgments and automated metrics, and use benchmark reporting templates that document each measure, evaluator, comparison method, and result.

23. Fine-grained medical Q&A evaluation pattern Build domain-specific Q&A datasets with documented construction criteria; label omissions and hallucinations separately; apply an item-level clinical error taxonomy; report trustworthiness metrics for answer validity, evidence support, clinical risk, and reliability; and use benchmark reporting templates that disclose dataset provenance, annotation decisions, limitations, and trustworthy-use boundaries.

24. Clinical education benchmark module For board-style or CME question benchmarks, score correctness, content omission, and risk of harm as independent endpoints; prespecify clinician adjudication rules for reviewer qualifications, disagreement resolution, and severity handling; and report results by specialty rather than relying only on pooled aggregate performance.

25. Fine-grained domain-specific medical Q&A dataset controls For trustworthy medical LLM benchmarking, evaluate domain-specific Q&A datasets with item-level error taxonomies, separate omission and harm labels, domain coverage checks, calibration by question type, and dataset provenance controls.

26. Fine-grained domain Q&A evaluation workflow Construct domain-specific Q&A datasets with documented source, inclusion, and evidence criteria; use clinician adjudication to label ambiguity, omissions, evidence grounding, and item-level error categories; assess calibration and clinically relevant subgroups; and report item-level findings alongside aggregate accuracy.

27. Intensive-care nursing comparative evaluation For intensive-care nursing questions, use expert reference answers, direct model-to-model comparison, clinically weighted error categories, and dedicated analysis of unsafe omissions; report these dimensions separately rather than relying on aggregate accuracy alone.

28. Fine-grained medical Q&A dataset quality controls Label item-level difficulty and ambiguity, assess answer-evidence alignment, apply separate omission and contradiction taxonomies, report clinically relevant subgroup results and annotator agreement, and check for dataset contamination.

29. Management-critical omission and harm adjudication Score correctness, content omission, and risk of harm as separate endpoints; require severity-weighted clinician adjudication; and explicitly report cases where fluent answers omit management-critical details.

30. Fine-grained domain Q&A evidence and reliability audit Evaluate each item with a prespecified error taxonomy; score omissions separately; verify supporting evidence; flag ambiguity and define handling rules; report clinically relevant subgroup results and inter-rater agreement; and check benchmark items and source data for dataset leakage.

31. Specialty-aware intensive-care nursing benchmark interpretation For intensive-care nursing questions, use clinician-authored scoring rubrics and reference answers, compare ChatGPT, DeepSeek, and Google Gemini under the same question set and conditions, and analyze safety-critical errors and omissions separately from overall accuracy. Treat board-style question accuracy as task-specific evidence that cannot establish bedside performance or safety without validation in clinical workflows.

32. Fine-grained medical Q&A validation and failure analysis Validate domain-specific medical Q&A datasets with an item-level taxonomy; score omissions and uncertainty separately; analyze clinically relevant subgroups; and explicitly check whether aggregate accuracy conceals clinically important failures.

33. Ophthalmology CME answer safety adjudication Score correctness, clinically material omission, and risk of harm separately; require blinded expert adjudication; and report harmful-error rates independently from overall answer accuracy.

34. Versioned fine-grained medical Q&A evaluation Evaluate each item with a prespecified error taxonomy; score omissions and potential harm separately; report results across clinical domain strata; assess confidence calibration; check for benchmark leakage; document adjudication and disagreement-resolution procedures; and version datasets, annotations, rubrics, and benchmark releases for reproducible comparison.

35. Systematic clinical-note quality benchmark framework Evaluate AI-generated clinical notes across factuality, completeness, hallucination, clinical usefulness, and harm-sensitive scoring; stratify results by note type; report inter-rater reliability; and validate automated metrics against clinician judgments before using them as clinical-quality proxies.

36. Fine-grained trustworthy domain Q&A evaluation Evaluate domain-specific Q&A datasets with item-level error taxonomies and separate ambiguity and clinically material omission labels; require specialty-expert adjudication; assess calibration and harmful answers; document dataset construction, provenance, annotations, and limitations; and report factual recall separately from clinically safe reasoning.

37. Clinical-note metric validity and expert-review controls Evaluate factual correctness, completeness, organization, clinical usefulness, hallucination, harmfulness, and style as distinct clinical-note quality dimensions; document each metric's validity and inter-rater reliability; and require expert review when automated scores lack demonstrated validity or agreement with expert judgment for the intended dimension and clinical context.

38. Blinded intensive-care nursing cross-provider error analysis Benchmark intensive-care nursing question answering across ChatGPT, DeepSeek, and Google Gemini under matched conditions using blinded clinician scoring, and explicitly analyze clinically consequential errors separately from aggregate accuracy.

39. Trustworthy fine-grained domain QA assessment Evaluate domain-specific medical Q&A with item-level error taxonomies, ambiguity and evidence-quality labels, clinically relevant subgroup analysis, expert adjudication, calibration checks, and dataset documentation covering construction, provenance, annotations, and limitations.

40. Professional-society guideline concordance evaluation Map each model recommendation to the applicable professional-society statement; score omissions and contradictions separately; and flag advice that exceeds or misstates the guideline's scope.

41. Plausibility-sensitive clinical answer adjudication Score correctness, clinically important omissions, and risk of harm as separate dimensions; report errors weighted by clinical severity; and require adjudicators to label factually plausible answers as unsafe or incomplete when omitted content or likely downstream use creates clinical risk.

42. AI-generated clinical-note quality benchmark dimensions
    For systematic reviews or experimental benchmarks of AI-generated clinical notes, evaluate factual correctness, omissions, hallucinations, clinical risk, style and format compliance, and inter-rater reliability as separate dimensions; use benchmark datasets with human clinician adjudication to compare evaluation methods.

43. Clinical-note rubric and safety regression design
    For AI-generated clinical-note evaluation, translate systematic review and experimental benchmark findings into prespecified rubrics that separate factuality checks, clinically material omissions, note-quality dimensions, inter-rater adjudication, and safety risk; use the same checks as regression tests before model, prompt, template, retrieval, or integration changes.

44. Fine-grained specialty-calibrated medical Q&A dataset evaluation
    For domain-specific medical Q&A datasets supporting trustworthy medical language models, use an item-level taxonomy; label omissions and potential harms separately; calibrate results by clinical specialty; run adversarial ambiguity and answerability checks; and report benchmark construction, annotation decisions, item-level findings, limitations, and trustworthy-use boundaries.

45. Domain-specific Q&A benchmark suitability review
    Before using a medical Q&A dataset as a trustworthy language-model benchmark, require documented dataset provenance and source-evidence links; specialty-stratified item coverage; an item-level error taxonomy for factual errors, hallucinated or unsupported content, ambiguity, and answerability; separate hallucination and omission scoring; calibration checks by item type or specialty; and explicit suitability criteria that state when the dataset is reliable enough for benchmark reporting.

46. AI-generated clinical-note evaluation method benchmark reporting
    For systematic review-informed experimental benchmarks of AI-generated clinical-note quality methods, compare rubric-based ratings, clinician-rated judgments, automated metrics, factuality checks, omission scoring, and harm-risk evaluations as distinct methods; report benchmark fields for note task and type, dataset provenance, comparator systems, evaluation method, evaluator type, rubric or metric definition, factuality, omission, and harm-risk endpoints, reliability or agreement results, and method-comparison findings.

47. Fine-grained domain-specific medical Q&A trustworthiness reporting
    For trustworthy medical LLM benchmarking, label each domain-specific Q&A item with answerability, ambiguity, evidence support, omission, harm, and uncertainty fields; stratify results by medical subdomain; assess uncertainty calibration within those strata; and document dataset construction, provenance, annotation rules, limitations, and trustworthy-use boundaries.

48. Ophthalmology CME-style evaluation
    For ophthalmology continuing medical education question benchmarks, score correctness, content omission, and risk of harm as separate endpoints; check response consistency with applicable guidelines or reference standards; and report results using specialty-stratified summaries rather than pooled accuracy alone.

49. ICU nursing scenario question-answering slice
    Compare frontier and open models on clinician-authored intensive-care nursing scenarios under matched conditions; report clinically important omissions and unsafe recommendations separately from accuracy; and require human clinical review before any deployment decision.

50. Trustworthy medical Q&A benchmark construction checks
    For domain-specific medical Q&A datasets, construct benchmarks with documented item selection, domain coverage checks, item-level taxonomy labels, and separate omission and hallucination annotations; report annotation practices, dataset limits, and trustworthy-use boundaries alongside model performance.

51. Stratified domain-specific medical Q&A reporting
    For domain-specific medical Q&A evaluation, stratify results by specialty, question type, evidence source, omission risk, calibration, and harm class; compare model performance within those strata and treat aggregate accuracy as a secondary summary rather than the sole trustworthiness claim.

52. Ophthalmology CME omission-harm separation
    For ophthalmology CME-style benchmarks, require scoring schemes that distinguish harmless incompleteness from omissions or recommendations that create clinical risk; report correctness, content omission, and risk of harm as separate dimensions.

53. Fine-grained domain-specific medical QA grading pattern
    For domain-specific medical Q&A evaluation, define question taxonomies, assign separate omission and harm labels, grade answers against supporting evidence, check annotator agreement, analyze clinically relevant subgroups, and report calibration rather than relying on aggregate accuracy alone.

54. Systematic-review-backed clinical-note evaluation comparison
    For AI-generated clinical-note benchmarks, prespecify rubric-based correctness, omission, and harm endpoints; include inter-rater checks; document benchmark dataset provenance and note type; and compare evaluation methods experimentally under matched inputs, outputs, rubrics, evaluator types, and reporting fields.

55. ICU nursing question-answering benchmark scenario
    Treat intensive-care nursing question answering as a specialty benchmark: validate nursing-domain items and reference answers, compare ChatGPT, DeepSeek, and Google Gemini under matched prompts and conditions, score clinically important omissions and potential harm separately from correctness, and avoid extrapolating board-style QA performance to bedside nursing support without clinical workflow validation.

56. Core AI-generated clinical-note quality reference
    Use the 2026 systematic review and experimental benchmark of AI-generated clinical note quality methods as the core evaluation reference; design evaluations that separately cover correctness, omissions, harm risk, factuality, rubric design, inter-rater adjudication, and regression-test datasets.

57. Fine-grained specialty and reasoning-type Q&A adjudication
    For domain-specific medical Q&A dataset benchmarking, stratify questions by specialty and reasoning type; score answer correctness separately from omissions and unsafe overreach; track dataset provenance throughout construction and reporting; and use reviewer adjudication to support trustworthy medical model comparisons.

58. Ophthalmology CME explicit harm taxonomy For ophthalmology-style continuing medical education evaluations, score correctness, content omission, and risk of harm as separate criteria; require specialty expert adjudication; define explicit harm taxonomy labels for unsafe recommendations, delayed-care risk, misleading reassurance, and clinically material omissions; and use aggregate accuracy only as a secondary summary.

59. Fine-grained Q&A dataset curation before model comparison Before using domain-specific medical Q&A datasets to compare medical LLMs, run item-level quality checks for answerability, evidence support, ambiguity, omissions, and clinical risk; label ambiguity and clinically material omissions explicitly; define trustworthy-answer rubrics for correctness, completeness, uncertainty, and source support; and curate or exclude unsuitable items before benchmark reporting.

60. Clinical-note rubric strengthening from method benchmarks For AI-generated clinical note evaluation, build rubrics and benchmark datasets that explicitly separate correctness, omissions, harmfulness, factual consistency, note completeness, inter-rater reliability, and dataset construction details so quality methods can be compared experimentally.

61. Fine-grained trustworthy medical Q&A dataset governance For domain-specific medical Q&A benchmarks, specify item-level error taxonomies, separate omission and harm scoring, calibration checks, specialty-stratified reporting, and dataset governance fields for provenance, annotation rules, limitations, and trustworthy-use boundaries.

62. Systematic-review-backed clinical-note rubric comparison For AI-generated clinical-note benchmarks, construct datasets with documented note type and provenance; evaluate correctness, factuality, omissions, note completeness, and risk of harm as separate endpoints; report inter-rater agreement; and experimentally compare evaluation rubrics under matched benchmark inputs and outputs.

63. Clinical-note evaluation method evidence source Use the 2026 systematic review and experimental benchmark of AI-generated clinical note evaluation methods as a core evidence source; evaluate correctness, completeness, omission detection, and harm scoring separately; include inter-rater comparison; and document the limits of automated evaluation relative to human evaluation.

64. Clinical-note quality rubric expansion Expand AI-generated clinical-note evaluation rubrics into separate scoring fields for correctness, clinically material omissions, hallucinations or unsupported content, patient-safety risk, note structure and format compliance, inter-rater reliability, and benchmark design; compare evaluation methods experimentally with fixed inputs, outputs, evaluator types, rubric definitions, and reporting fields.

65. Nursing-facing ICU question benchmark pattern For intensive-care nursing question evaluations, build role-specific gold standards with nursing-domain experts, compare ChatGPT, DeepSeek, and Google Gemini on the same items and prompts, and conduct unsafe-answer review for recommendations, omissions, or overconfidence that could affect nursing decisions. Treat nursing-facing clinical LLM evaluation as requiring domain expert adjudication rather than generic medical QA scoring.

66. Repeatable trustworthy medical Q&A benchmark reporting For domain-specific medical Q&A datasets supporting trustworthy medical language models, predefine item-level error taxonomy, separate omission and potential-harm labels, dataset provenance checks, specialty-level calibration analysis, and repeatable benchmark report fields for construction criteria, annotation rules, item-level findings, limitations, and trustworthy-use boundaries.

67. Plastic-surgery guideline-concordance adjudication When evaluating specialty chatbot recommendations against society guidance such as ASPS and AAPS plastic surgery guidance, compare each recommendation to the relevant society-guideline statement; score clinically material omissions and contraindications separately; flag unsafe recommendations, conflicts, and scope drift where advice exceeds, narrows, or misstates the guidance; and require specialty expert adjudication for procedural advice before using guideline-concordance scores in safety conclusions.

68. Ophthalmology board-style frontier model comparison For ophthalmology board-style question benchmarks comparing frontier models such as Gemini 3 Pro and GPT-5 family models, grade responses against specialty-specific gold answers; report exact model family, version, and evaluation date; assess confidence calibration; and separate exam-question performance from clinical readiness or patient-care safety claims.

69. Ophthalmology CME benchmark template with partial-credit safety tracking For ophthalmology CME-style question benchmarks, use a template that scores correctness, partial correctness, content omission, missing safety-critical content, and risk of harm as separate fields; explicitly track partially correct answers and omissions that could affect clinical safety rather than collapsing them into overall accuracy.

70. Fine-grained domain-specific medical Q&A label set For domain-specific medical Q&A evaluation, score correctness, completeness, omission, uncertainty, grounding, specialty coverage, and harm-risk as separate item-level or answer-component labels; use aggregate accuracy only as a secondary summary because it can conceal clinically important failures across those dimensions.

71. Domain-specific medical Q&A dataset curation audit For trustworthy medical language model benchmarks, evaluate each domain-specific Q&A item with a prespecified error taxonomy; score omissions and potential harm independently from correctness; report trustworthiness dimensions such as answerability, ambiguity, evidence support, uncertainty, reliability, and clinical risk; and curate benchmark items by checking provenance, source-evidence alignment, annotation consistency, domain coverage, limitations, and trustworthy-use boundaries.

72. Systematic review and experimental clinical-note benchmark methods For AI-generated clinical-note quality benchmarks, use systematic review-informed experimental method comparisons that separately evaluate factuality, completeness, clinical usefulness, hallucination, and reviewer calibration; report benchmark templates with note task and type, dataset provenance, evaluator type, rubric or metric definition, scoring dimension, agreement or calibration results, and method-comparison findings.

73. Fine-grained domain-specific medical Q&A benchmark documentation For clinical LLM evaluations using domain-specific medical Q&A datasets, document item-level error taxonomy, trustworthiness scoring fields, specialty stratification, answer omission tracking, annotation decisions, item-level findings, limitations, and trustworthy-use boundaries.

74. Documentation-assistant clinical-note regression datasets For AI-generated clinical-note documentation assistants, use the 2026 systematic review and experimental benchmark of clinical-note quality methods to select evaluation methods that separate note-level factuality checks, clinically material omissions, risk taxonomy labels, inter-rater reliability reporting, and reusable regression datasets before prompt, model, retrieval, template, or workflow changes.

75. Versioned ICU nursing question benchmark case Treat intensive-care nursing questions as a specialty evaluation case for comparing ChatGPT, DeepSeek, and Google Gemini: document each item's source and reference-answer provenance, capture model names, versions, and evaluation dates, and perform nursing-safety harm review that separates unsafe recommendations, clinically important omissions, and correctness from aggregate performance.

76. Fine-grained Q&A dataset readiness before model selection Before selecting models from domain-specific medical Q&A benchmark results, audit dataset quality at the item level with an explicit error taxonomy, answerability and ambiguity checks, omission scoring, evidence-support review, and trustworthy-model reporting fields that disclose dataset limits and benchmark-use boundaries.

77. Fine-grained clinical Q&A trustworthiness reporting For domain-specific clinical Q&A datasets, score each item with an error taxonomy that separates factual errors, unsupported claims, omissions, and potential harm; calibrate results by question type; check source-grounding for reference answers and model responses; and report item-level trustworthiness findings alongside aggregate accuracy rather than relying on aggregate accuracy alone.

78. Fine-grained medical Q&A taxonomy and calibration checks For domain-specific medical Q&A evaluation, define question taxonomies and difficulty labels before scoring; assign separate omission and potential-harm labels; stratify results by medical subdomain and difficulty; preserve reference provenance and source-evidence links; assess calibration; and report inter-rater agreement checks for item labels and adjudicated scores.

79. Fine-grained domain-specific medical Q&A adjudication limits For domain-specific medical Q&A evaluation, stratify dataset items by question type and clinical domain; use separate omission and harm taxonomies; require expert adjudication with inter-rater agreement reporting; calibrate results by question type; and report model limitations separately from raw accuracy.

80. Rubric-driven domain-specific medical Q&A dataset evaluation For trustworthy medical LLM benchmarks using domain-specific Q&A datasets, design specialty-calibrated rubrics before scoring; apply item-level error taxonomies for ambiguity, answerability, evidence support, unsupported or hallucinated content, omissions, and potential harm; score omissions and harm independently from correctness; and document dataset construction, provenance, annotation rules, item-level findings, limitations, and trustworthy-use boundaries.

81. Expanded clinical-note quality rubric controls For AI-generated clinical-note quality evaluations, expand rubrics into separate fields for factuality, completeness, note structure, safety risk, provenance, inter-rater agreement, and reusable regression testing so systematic review-informed method comparisons can distinguish documentation quality dimensions instead of collapsing them into one score.

82. Fine-grained domain-specific medical Q&A curation and trustworthiness checks For domain-specific medical Q&A datasets, curate items before benchmarking by documenting source provenance, inclusion criteria, reference-answer evidence, annotation decisions, and exclusion rules; score each item with an error taxonomy that separates factual accuracy, educational value, unsupported content, omissions, answer-evidence alignment, and potential harm risk; and report item-level trustworthiness findings alongside aggregate model scores.

83. Systematic clinical-note rubric benchmark design For AI-generated clinical-note quality evaluations, design systematic review-informed rubrics that separately score correctness, completeness, hallucination or unsupported content, safety risk, documentation utility, and inter-rater reliability; use experimental benchmark designs that compare evaluation methods under documented inputs, outputs, evaluator types, rubric definitions, and reporting fields.

84. ICU nursing QA protocol-validation slice For intensive-care nursing question-answering benchmarks comparing ChatGPT, DeepSeek, and Google Gemini-style systems, define a specialty-specific item taxonomy, standardize prompts and model conditions across providers, tag clinically important omissions and potential harms separately from correctness, and validate answers against ICU nursing protocols rather than generic medical QA scoring.

85. Fine-grained trustworthy medical Q&A item scoring For domain-specific medical Q&A datasets supporting trustworthy medical language model benchmarks, score each item for correctness, missing content, uncertainty, provenance, hallucination or unsupported content, and harm risk; use prespecified adjudication rules for ambiguous, incomplete, uncertain, or potentially harmful answers, and report item-level findings alongside aggregate benchmark summaries.

86. ICU nursing scope-of-practice QA benchmark case For intensive-care nursing question-answering evaluations, compare ChatGPT, DeepSeek, and Google Gemini under matched prompts and conditions; score answer completeness separately from correctness; require nursing-scope safety review for recommendations, omissions, or overconfident advice; and define escalation criteria when model recommendations exceed nursing practice boundaries.

87. Fine-grained domain-specific medical Q&A readiness reporting For domain-specific medical Q&A datasets supporting trustworthy medical language models, define an item taxonomy; label omissions and contradictions separately; grade potential harm; preserve evidence traceability for reference answers and model claims; calibrate results by question type; and report benchmark accuracy separately from clinical readiness.

88. Fine-grained domain-specific medical Q&A dataset evaluation practices For domain-specific medical Q&A datasets used in trustworthy medical LLM benchmarks, audit item-level errors with prespecified categories, classify omissions and potential harm separately, review domain coverage before model comparison, and require reports to include provenance, annotation decisions, item-level findings, limitations, and trustworthy-use boundaries.

89. AI-generated clinical-note quality method reproducibility For AI-generated clinical-note quality evaluations, use systematic review and experimental benchmark evidence to prespecify rubric fields for factuality, omission, hallucination or unsupported content, clinical usefulness, inter-rater agreement, and benchmark reproducibility; report the input set, output set, evaluator type, rubric definitions, and comparison procedure so evaluation methods can be compared transparently.

90. Clinical-note evaluation method selection and agreement reporting Use systematic review and experimental benchmark evidence when selecting AI-generated clinical-note evaluation rubrics; score correctness, clinically material omissions, and harm risk as separate endpoints; report inter-rater agreement; and document benchmark comparison methods, rubric definitions, evaluator types, and reporting fields.

91. Clinical-note deployment-claim benchmark gate Before making deployment claims for AI-generated clinical notes, compare evaluation methods on benchmark datasets, separate correctness from omission and harm endpoints, track inter-rater reliability, and require clinician adjudication of benchmark results.

92. Intensive-care nursing QA rubric slice For intensive-care nursing QA comparisons of ChatGPT, DeepSeek, and Google Gemini, score guideline fidelity, medication and dose safety, escalation language, omitted contraindications, and unsafe confident answers as separate rubric dimensions; require human clinical review of answers that are unsafe despite confident wording.

93. General-versus-medical LLM Q&A calibration audit For domain-specific medical Q&A datasets, tag each item by domain, apply an item-level error taxonomy, preserve evidence attribution for reference answers and model claims, score uncertainty and harm risk separately, check for dataset leakage, and calibrate results across general-purpose and medical LLMs before reporting trustworthy benchmark conclusions.

94. ICU nursing QA consistency and local validation gate For intensive-care nursing question-answer evaluation, build specialty-specific item banks, compare frontier and open models under matched prompts and conditions, run answer-consistency checks across repeated attempts or model settings, define escalation criteria for unsafe nursing advice, and require local validation before clinical education or workflow use.

95. Fine-grained domain-specific medical Q&A benchmark adjudication For trustworthy medical model benchmarks using domain-specific Q&A datasets, stratify items by difficulty, label omissions and potential harms separately, define reviewer disagreement adjudication procedures, summarize calibration by clinically relevant item strata, and document dataset construction, provenance, annotation rules, limitations, and trustworthy-use boundaries.

96. Fine-grained medical Q&A model-comparison reporting For trustworthy medical language model benchmarks using domain-specific Q&A datasets, annotate each item with scoring fields for answer validity, omissions, potential harm, ambiguity, and domain coverage; compare candidate models under the same item set and scoring rubric; and report results with templates that separate item-level findings, domain coverage analysis, model-comparison summaries, annotation decisions, limitations, and trustworthy-use boundaries.

97. Systematic clinical-note evaluation reliability and residual-risk reporting For AI-generated clinical-note evaluations informed by systematic review and experimental benchmark evidence, prespecify rubrics that separately score correctness, clinically material omissions, note completeness, and safety risk; construct benchmark datasets with documented note task, note type, dataset provenance, evaluator type, rubric definitions, and method-comparison fields; report inter-rater reliability and residual risk so automated or rubric-based quality conclusions are not overstated.

98. AI-generated clinical-note quality method benchmark design Use the 2026 systematic review and experimental benchmark of AI-generated clinical-note quality methods to design evaluations that separately assess correctness, omissions, hallucination, completeness, clinical usefulness, harm risk, and inter-rater agreement; document benchmark design fields so evaluation methods can be compared under matched clinical-note tasks, inputs, outputs, evaluator types, and rubric or metric definitions.

99. Domain-specific medical Q&A benchmark reporting template For fine-grained evaluation of domain-specific medical Q&A datasets, require item-level error taxonomy fields, trustworthiness scoring for answer validity and evidence support, separate omission and harm labels, dataset provenance checks, annotation-rule documentation, item-level finding summaries, limitations, and trustworthy-use boundary reporting.

100. Note-specific clinical quality dimensions For AI-generated clinical-note quality evaluation, score factual correctness, omissions, hallucinations, readability, billing or compliance risk, clinician edit burden, and inter-rater reliability as separate note-specific dimensions; use these fields when comparing human review, rubric-based review, and automated evaluation methods.

101. Specialty-calibrated medical Q&A trustworthiness limits For domain-specific medical Q&A dataset evaluation, use item-level error taxonomy, separate omission and harm scoring, calibration across specialties, reproducible benchmark metadata, and trustworthy reporting of dataset limits, annotation choices, and use boundaries rather than aggregate accuracy alone.

102. Systematic-review-backed clinical-note documentation workflow evaluation For AI-generated clinical-note documentation workflows, design rubrics and experimental benchmark datasets that separately assess correctness, completeness, harmful omissions, style and usability, and inter-rater reliability; compare evaluation methods under documented benchmark construction rather than treating a single metric as sufficient.

103. Fine-grained domain-specific medical Q&A trustworthiness workflow For trustworthy medical language model benchmarks using domain-specific Q&A datasets, score each item for correctness, omission, harm risk, ambiguity, evidence support, and specialty slice; run dataset quality checks for provenance, answerability, evidence alignment, coverage, annotation consistency, and limitations; report calibration by relevant item or specialty strata; and use a prespecified adjudication workflow for reviewer disagreement and clinically important failures.

104. AI-generated clinical-note quality benchmark templates For systematic review-informed experimental benchmarks of AI-generated clinical-note quality methods, define separate evaluation dimensions for correctness, omissions, hallucinations, risk of harm, documentation completeness, inter-rater reliability, and rubric design; use benchmark reporting templates that document the note task, dataset provenance, evaluator type, rubric or metric definition, scoring endpoint, agreement results, and method-comparison findings.

105. Governed fine-grained domain-specific medical Q&A evaluation For trustworthy medical LM benchmarks using domain-specific Q&A datasets, define an item-level error taxonomy, score omissions and potential harm separately, check domain coverage before comparison, assess calibration by item or domain strata, record reproducibility metadata for dataset version, annotations, prompts, model settings, and evaluation date, and document benchmark governance decisions including limitations and trustworthy-use boundaries.

106. AI-generated clinical-note quality adjudication design For systematic review-informed experimental benchmarks of AI-generated clinical-note quality methods, score factuality, omissions, safety risk, note structure, and completeness as separate dimensions; report inter-rater reliability; document limits of automated metrics; and design human adjudication with prespecified reviewer roles, rubrics, disagreement resolution, and final adjudication rules.

107. Core clinical-note quality methods reference Use the 2026 systematic review and experimental benchmark of AI-generated clinical note quality methods as the core evaluation reference; require rubrics and reports to separately document correctness, omissions, clinical risk, rubric design, inter-rater review, and benchmark reporting requirements.

108. Pre-scoring domain-specific medical Q&A audit Before model scoring, audit domain-specific medical Q&A datasets at the item level; classify question type and clinical risk; label content omissions and potential harm explicitly; and report factual correctness separately from trustworthiness and deployment readiness.

109. Task-specific clinical-note rubric strengthening For AI-generated clinical-note evaluations, use systematic review and experimental benchmark evidence to strengthen task-specific note rubrics with separate fields for factuality, completeness, hallucination or unsupported content, usability, safety risk, inter-rater reliability, and note-type-specific quality metrics; compare rubric, human-review, and automated methods under the same benchmark task before treating any score as a clinical-quality proxy.

110. Human-versus-automated clinical-note scoring benchmark For AI-generated clinical-note quality workflows, design systematic review-informed experimental benchmarks that compare human clinician scoring with automated evaluation methods across correctness, omission detection, harm-risk classification, rubric-defined documentation quality, inter-rater agreement, and repeatable regression tests for model, prompt, template, retrieval, or documentation workflow changes.

111. Systematic review clinical-note quality benchmark reporting For AI-generated clinical-note quality benchmarks, use systematic review and experimental benchmark guidance to define rubrics and reporting templates that separately document correctness, omissions, harm risk, note completeness, rubric design, inter-rater reliability, evaluator type, metric definitions, and method-comparison findings.

112. Core evaluation-methods reference for AI-generated clinical notes Use the 2026 systematic review and experimental benchmark of AI-generated clinical note quality methods as the core reference for evaluation-method selection; require separate correctness, omission, harm-risk, and note-quality rubric fields; define inter-rater adjudication and benchmark dataset design before scoring; and report results with templates that document evaluator type, rubric or metric definition, agreement findings, and method-comparison results.

113. ICU nursing specialty benchmark pattern For intensive-care nursing question evaluation, separate factual correctness, triage safety, nursing-scope boundaries, escalation language, and guideline concordance as distinct rubric fields; compare frontier and open reasoning models only under matched prompts, clinician-reviewed scoring, and explicit non-deployment assumptions.

114. Fine-grained domain Q&A dataset auditing For domain-specific medical Q&A datasets, classify question type, answerability, evidence sufficiency, omission risk, harmfulness, calibration, and inter-rater adjudication so medical LLM benchmarks test trustworthy behavior rather than aggregate accuracy alone.

115. Trustworthy medical Q&A comparison templates For domain-specific medical Q&A benchmark comparisons, design item-level rubrics, tag omissions and potential harms separately, report results by specialty strata, check calibration, review benchmark items for dataset leakage, and use reporting templates that document dataset construction, annotation rules, item-level findings, limitations, and trustworthy-use boundaries.

116. ICU-nursing specialty-stratified actionability benchmark pattern For intensive-care nursing question benchmarks, stratify questions by relevant nursing specialty or topic, compare providers under matched conditions, and use blinded nurse-expert adjudication. Score factual accuracy separately from clinically material omissions and potential harm, weight omissions and errors by adjudicated severity, and report bedside actionability independently from factual accuracy. Record model identifiers and evaluation dates without treating the observed cross-provider ranking as durable.

117. Fine-grained domain Q&A failure analysis Evaluate domain-specific medical Q&A beyond aggregate accuracy by assessing item answerability, evidence support, semantic correctness, clinically important omissions, prespecified error-taxonomy categories, clinically relevant subgroup performance, and inter-rater agreement; include adjudicated examples of representative failures.

118. Fine-grained medical Q&A adjudication-to-regression protocol For each item, apply a prespecified error taxonomy; score correctness and completeness as separate dimensions; flag potentially harmful omissions; and record evidence attribution for the reference answer and evaluated claims. Report performance by clinical domain and item difficulty, measure annotator agreement and adjudicate disagreements, check for dataset leakage, assess calibration within relevant slices, and document dataset construction, provenance, versions, annotation rules, and limitations. Convert adjudicated errors into versioned regression cases that retain the input, expected evidence-supported answer elements, error labels, severity, and pass/fail criteria; rerun them after model, prompt, retrieval, or evaluation changes, using aggregate accuracy only as a secondary summary.

119. Intensive-care nursing question-answering benchmark profile Grade intensive-care nursing answers against nurse-authored reference answers using specialty-specific error categories; track provider, exact model version, and evaluation date; score clinically material omissions and potential harm separately; assess whether stated uncertainty is calibrated to answer correctness and risk; and treat board-style accuracy as benchmark-specific evidence, not evidence of bedside readiness without clinical workflow validation.

120. Item-level domain-specific medical Q&A dataset construction and release audit Construct and audit domain-specific medical Q&A datasets item by item: verify taxonomy coverage, answerability, ambiguity, and reference-answer quality; measure and report inter-rater agreement; check for benchmark contamination; assign fine-grained error labels; analyze clinically relevant subgroups; and publish trustworthy release documentation covering construction, provenance, annotation rules, limitations, intended uses, and use boundaries.

121. Intensive-care nursing benchmark profile with drift controls Stratify intensive-care nursing questions by profession-specific topic and task type; compare ChatGPT, DeepSeek, and Google Gemini under matched conditions; weight errors by clinical consequence; review clinically material omissions and potential harm separately from correctness; assess uncertainty calibration within question strata; check items for question leakage; and record model versions and evaluation dates so results can be re-evaluated for model-version drift.

122. Fine-grained medical Q&A evaluation protocol Define a question taxonomy and prespecify specialty and difficulty strata; construct source-grounded reference answers with traceable evidence; label omissions separately from other error modes; use qualified clinician adjudication with disagreement resolution; evaluate confidence calibration and abstention behavior; analyze clinically relevant subgroups; check for benchmark contamination; record the exact model version, prompt, and evaluation date; and report item- and stratum-level failures, uncertainty, and limitations alongside aggregate accuracy.

123. Reproducible intensive-care nursing benchmark slice Use nurse-authored intensive-care questions and specialty-expert scoring to compare ChatGPT, DeepSeek, and Google Gemini; analyze clinically material omissions and potential harm separately from accuracy; report inter-rater agreement; and record the provider, exact model/version, and evaluation date for reproducible comparisons. Treat board-style accuracy as benchmark-specific evidence that does not establish bedside readiness.

124. Pre-benchmark domain-specific medical Q&A dataset audit Before benchmarking, verify each item's answerability and the currency of its supporting evidence; detect ambiguous or contaminated items; stratify items by topic and difficulty; define fine-grained error and harm taxonomies; and report performance with item-level provenance rather than relying on aggregate accuracy.

125. ICU-nursing benchmark profile with locked models and paired adjudication For intensive-care nursing questions, lock and report the exact provider and model version, prompt, decoding settings, and evaluation date; stratify questions and results by ICU-nursing specialty or topic and prespecified difficulty; use independent nurse-expert review with disagreement adjudication; compare models with paired statistical tests on the same items; and score correctness, clinically material omissions, harmful advice, uncertainty, and appropriate abstention as separate endpoints.

126. Fine-grained medical Q&A dataset audit with slice uncertainty Audit domain-specific medical Q&A datasets for answerability and ambiguity; measure annotation agreement; screen for contamination and lexical leakage; stratify results by specialty and difficulty; assess calibrated correctness and evidence attribution; classify omissions and potential harms with separate taxonomies; and report confidence intervals for each slice.

127. Specialty-user intensive-care nursing benchmark pattern For intensive-care nursing questions, stratify results by knowledge domain and clinical risk; compare models under identical prompts; measure clinically important omissions and unsafe advice separately from accuracy; and require nurse-expert adjudication before drawing deployment conclusions.

128. Expert-authored, versioned medical Q&A benchmark workflow Construct items and reference answers with qualified domain experts; assign domain and difficulty labels; audit ambiguity and answerability before scoring; check items and source material for benchmark contamination; apply granular, prespecified error taxonomies; assess confidence calibration; report clinically relevant subgroup results; and version dataset items, annotations, rubrics, prompts, model settings, and benchmark releases for reproducible comparison.

129. Closed-system radiography-response evaluation profile For closed-system LLM evaluations of radiography responses, score factual correctness, scope-of-practice errors, unsupported recommendations, omissions, and harm potential as separate dimensions; document whether retrieval or external references were unavailable during testing.

130. Fine-grained medical Q&A safety evaluation module Evaluate factual correctness, evidence support, completeness, uncertainty calibration, subgroup and topic performance, and clinically consequential error types as separate dimensions; include dataset documentation, expert adjudication, item-level failure analysis, confidence intervals, and contamination checks so aggregate accuracy does not conceal unsafe domain-specific behavior.

131. Intensive-care nursing specialty evaluation pattern For intensive-care nursing questions, grade responses against clinician-authored reference answers using prespecified nursing-scope safety criteria; capture the exact model version and evaluation date; use blinded expert grading with disagreement adjudication; analyze confidence calibration and appropriate abstention; compare models with paired statistical methods on the same questions; and score factual correctness, clinical prioritization, escalation decisions, and potential patient harm as separate endpoints.

132. Fine-grained medical Q&A competency and failure-mode analysis Construct and analyze medical Q&A benchmarks with item-level competency taxonomies and traceable evidence provenance; label ambiguity, answerability, omissions, and potential harm separately; check for benchmark leakage; measure expert inter-rater agreement; analyze errors across clinically relevant subgroups; assess confidence calibration; and distinguish aggregate accuracy from clinically important failure modes in reporting.

Inputs / Outputs

Inputs

• Clinical LLM use case, target users, intended care setting, and non-use boundaries.
• Candidate model outputs, prompts, retrieval settings, system configuration, and comparison baselines.
• Benchmark dataset or dataset specification, including provenance and governance constraints.
• Reference notes, source records, guidelines, gold annotations, or clinician-authored comparison material when available.
• Safety taxonomy, rubric requirements, reviewer pool, and acceptable clinical risk thresholds.

Outputs

• Clinical LLM evaluation plan with task scope, hypotheses, metrics, and reviewer workflow.
• Dataset provenance and readiness checklist.
• Clinician adjudication rubric with label definitions and severity levels.
• Automatic and human evaluation matrix matched to the clinical task.
• Omission, hallucination, contradiction, and harm scoring summary.
• Regression gate checklist for model, prompt, retrieval, or integration changes.
• Final benchmark report with deployment recommendation, residual risks, and required follow-up validation.

References

• PubMed: Dahlberg A, Käenniemi T, Winther-Jensen T, Tapiola O, Luisto R. "Measuring the quality of AI-generated clinical notes: A systematic review and experimental benchmark of evaluation methods." Artificial Intelligence in Medicine. 2026 Jul. https://pubmed.ncbi.nlm.nih.gov/41955894/
• PubMed: Zhong W, Fu Y, Peng D, Liu Y, Liu Y. "Open-Source Large Language Models Distilled DeepSeek-R1 Pose Challenges for On-Premises Clinical Deployment in Medical Diagnosis: A Comparative Study of Performance." *Journ

Truncated - read the full file at https://github.com/mdbabumiamssm/AI-Agentic-Skills-by-Dr.-Mia/blob/f38b5163fa035ce7be569ccc6fa7b0f5e54dcb6f/Skills/Clinical/ClinicalLlmEvaluationBenchmarking_Agent/SKILL.md.