Pathologist-Authored Diagnostic Questions

• Pathologist-authored questions are expert-designed queries that benchmark diagnostic reasoning in computational pathology.
• They are constructed using multi-scale image analysis, clinical edge cases, and rigorous manual review to ensure accuracy.
• Their integration in AI training enhances spatial reasoning and model explainability, driving clinical and research advancements.

Pathologist-authored questions are rigorously constructed, expert-curated queries targeting diagnostic, interpretive, and reasoning competencies in computational pathology. These questions serve as the gold standard for benchmarking the capabilities of vision-LLMs (VLMs), multimodal large models (LMMs), and specialized artificial intelligence systems in understanding, analyzing, and reasoning over pathology images and diagnostic workflows. By emulating the real-world decision paradigms of practicing pathologists, these queries expose both the strengths and limitations of automated models in spatial reasoning, classification, localization, differential diagnosis, and explainability.

1. Definition, Purpose, and Distinction

Pathologist-authored questions are defined by their creation and critical review by licensed medical professionals, typically board-certified pathologists or individuals with formal training in pathology. Unlike crowdsourced, automatically generated, or student-written questions, pathologist-authored items are drafted to capture clinically relevant features, diagnostic edge cases, and ambiguities reflecting practical diagnostic challenges. These questions are not limited to identification but include complex reasoning (e.g., subtyping, spatial localization, and multi-step inference), mirroring the cognitive processes involved in real-world clinical workflows (Buckley et al., 24 Nov 2025, He et al., 2020, Zhang et al., 16 May 2025, Lu et al., 2023).

2. Dataset Construction and Annotation Protocols

Several high-impact pathology datasets leverage pathologist-authored questions as foundational benchmarks:

• MultiPathQA—ExpertVQA subset: Comprises 128 MCQs authored by two practicing pathologists, strictly derived from 76 whole-slide images (WSIs) with diagnostically challenging features. Protocols required multi-scale reasoning and consensus review for ground-truth verification. All distractors and prompts are manually crafted to avoid linguistic bias (Buckley et al., 24 Nov 2025).
• PathVQA: Involves 32,795 QA pairs across 4,998 histopathology images, each question–answer item reviewed and corrected by medical professionals to ensure visual relevance and correctness. Annotation guidelines enforce single-finding focus and concise answers. Eight canonical question types are defined: what, where, when, whose, how, why, how much/many, yes/no (He et al., 2020).
• Patho-R1: Constructs a multi-tiered, reasoning-focused dataset using 660 pathology textbooks, public corpora, and targeted manual verification. Images and figure panels are aligned with body text and CoT (chain-of-thought) diagnostic workflows, stratified by subfield and difficulty, and filtered for clinical plausibility. Multiple task types are included—multiple choice, long-form analysis, complex open-ended reasoning, and multi-turn conversation (Zhang et al., 16 May 2025).
• PathChat (PathQABench-Public): Focuses on MCQs and free-text questions authored by a board-certified pathologist across 23 expert-selected TCGA cases, using both image-only and image-plus-context evaluation paradigms (Lu et al., 2023).

Common features of these datasets include explicit stratification by diagnostic category, rigorous ground-truth review, and meticulous avoidance of annotation artifacts that could cue models via superficial language.

3. Formal Characteristics and Question Taxonomy

Pathologist-authored questions encompass a diversity of forms tailored to key diagnostic and analytic tasks:

• Multiple-choice questions (MCQ): Prominent in MultiPathQA, Patho-R1, and PathChat. Items require selection among several plausibly confusable entities or features, often embedding spatial or morphological reasoning (e.g., "Which region contains necrosis?", "Which immunohistochemical panel best supports the most likely diagnosis?") (Buckley et al., 24 Nov 2025, Zhang et al., 16 May 2025, Lu et al., 2023).
• Spatial localization: Questions may target particular regions (e.g., "On this slide, which quadrant shows mitotic figures?"), necessitating multi-resolution visual navigation (Buckley et al., 24 Nov 2025).
• Morphologic and feature-based queries: Require integration of cellular, architectural, and staining characteristics (e.g., "Which area demonstrates lymphovascular invasion?") (Buckley et al., 24 Nov 2025).
• Free-text and CoT prompts: Encourage models to engage in descriptive analysis, step-wise justification, or differential diagnosis reflecting real pathologist consults (Zhang et al., 16 May 2025, Lu et al., 2023).
• Expert explanation attributes: In domains like hematology, explanations formulated by pathologists may encode attribute-level assessments, such as granularity, cytoplasm color, nucleus shape, size relative to RBC, and N:C ratio (Pal et al., 2023).
• Difficulty tuning: Datasets may stratify question items as "easy," "medium," or "hard" based on requirement for ancillary interpretation and depth of reasoning, using both manual and unsupervised clustering approaches (Zhang et al., 16 May 2025).

4. Evaluation Frameworks and Metrics

Evaluation of model performance on pathologist-authored questions is grounded in explicit classification metrics, with protocol variation by question modality:

• Simple accuracy: For MCQ or single-answer VQA, defined as the proportion of correct responses out of total questions (e.g., GIANT ExpertVQA accuracy = correct model responses / 128) (Buckley et al., 24 Nov 2025, Lu et al., 2023).
• Bootstrap confidence intervals: Applied to accuracy estimates for statistical robustness (Buckley et al., 24 Nov 2025, Lu et al., 2023).
• Balanced accuracy, precision, recall, F1-score: Employed for multi-label or classification tasks, but not universally reported for all pathologist-authored subsets (Pal et al., 2023).
• Jaccard index and Dice coefficient: Used for benchmarking localization and segmentation performance where ground-truth spatial annotation is present (Pal et al., 2023).
• Faithfulness indices: For explanation tasks, comparison of conditional attribute distributions P(E|C=c) between ground truth and model-predicted values. Attribute-level match rates are validated by pathologist review (Pal et al., 2023).
• Human expert preference ranking: Open-ended question performance is often adjudicated by blinded human expert rating, using correctness, factual completeness, and terminology fidelity as scoring axes (Lu et al., 2023).
• Noise handling and annotation reliability: Some benchmarks implement bi-level optimization ("learning-by-ignoring") to control for noisy or non-expert-verified annotations, learning weights $a_i \in [0,1]$ for each training instance to exclude unreliable data (He et al., 2020).

No consensus metrics exist for inter-pathologist agreement or question difficulty estimation in MCQ subsets; such statistics are often unreported.

5. Integration in Model Training and Benchmarking

Pathologist-authored questions anchor state-of-the-art benchmarking frameworks and inform both the supervised and reinforcement learning pipelines central to modern pathology AI systems:

• Supervised fine-tuning (SFT): High-quality, expert-derived question–answer pairs drive instruction tuning, enhancing alignment with clinical reasoning paradigms (Zhang et al., 16 May 2025, Lu et al., 2023).
• Chain-of-thought (CoT) prompting and reward modeling: RL stages optimize for both factual accuracy (R_acc) and reasoning structure (R_fmt, R_len) as formalized in GRPO/DAPO objectives (Zhang et al., 16 May 2025).
• Zero-shot evaluation: Pathologist-authored questions serve as robust out-of-distribution tests of generalization; high-performing models (e.g., PathChat, Patho-R1) outperform baseline VLMs and closed-domain models in accuracy and preference-based evaluations (Buckley et al., 24 Nov 2025, Zhang et al., 16 May 2025, Lu et al., 2023).
• Instructional diversity: Prompts include multi-turn conversations, free-text reasoning, forced-choice, and attribute explanations, preparing models for adaptive responses in both educational and clinical settings (Buckley et al., 24 Nov 2025, Lu et al., 2023).
• Diagnostic workflow emulation: Advanced agentic frameworks (e.g., GIANT) demonstrate improved accuracy by enabling models to iteratively pan and zoom WSIs, closely mirroring pathologist slide navigation (Buckley et al., 24 Nov 2025).

Representative performance metrics are summarized below for pathologist-authored MCQ benchmarks:

Model	ExpertVQA Accuracy (%)	PathQABench-Public (MCQ)
GPT-5 + GIANT (5-run)	62.5 ± 4.4	—
TITAN	43.8 ± 4.2	—
SlideChat	37.5 ± 4.3	—
PathChat (image+ctx)	—	87.0 (CI 0.696–1.000)
GPT4V (image+ctx)	—	69.6

6. Limitations, Challenges, and Future Directions

Despite meticulous curation, several challenges persist:

• Annotation intensity: Pathologist time is expensive; scale-up of pathologist-authored items is limited by resource constraints. Many datasets use automated or semi-automated generation with selective manual review (He et al., 2020, Wu et al., 13 Aug 2024).
• Coverage and generalization: Rare entities and highly specialized diagnostic tasks remain underrepresented; model performance degrades on uncommon cases (Wu et al., 13 Aug 2024, Lu et al., 2023). This suggests that expansion of expert-authored benchmarks focused on rare diseases or ancillary modalities (IHC/FISH) is a current need.
• Noise and ambiguity: Even expert-derived annotations can harbor subjectivity or latent ambiguity. Methods such as learning-by-ignoring and RL-based reward control are applied to downweight noisy items (He et al., 2020, Zhang et al., 16 May 2025).
• Evaluation consistency: Lack of multi-reader adjudication in many open-ended evaluations impedes systematic assessment of model generalizability (Lu et al., 2023). A plausible implication is the need for multi-institutional, multi-expert benchmarks for robust comparative analysis.
• Instructional inflexibility: Many current pathologist-authored MCQs lack the compositional breadth of clinical narratives; future benchmarks may benefit from embedding real-world multi-modal context, nuanced clinical vignettes, and more extensive conversation turns (Buckley et al., 24 Nov 2025, Zhang et al., 16 May 2025, Lu et al., 2023).

7. Significance in Computational Pathology and Model Development

Pathologist-authored questions are central to advancing AI in pathology along several axes:

• Benchmarking expertise: These questions serve as objective standards against which AI model diagnostic, spatial, and reasoning abilities are assessed, allowing quantitative measurement of model progress and gap analysis relative to human-level pathology expertise.
• Driving explainability: Attribute-level and free-text expert explanations facilitate diagnosis transparency and model auditing, moving interpretability toward clinical acceptability (Pal et al., 2023).
• Guiding agentic reasoning: Agentic navigation paradigms, when evaluated on spatially demanding expert-authored items, exhibit clear performance gains versus static patching or thumbnail-based approaches, highlighting the necessity of workflow emulation in model design (Buckley et al., 24 Nov 2025).
• Curricular and educational deployment: Robust expert-derived question banks support digital pathology education, enabling automated tutoring, exam simulation, and cohort-based analytics (Lu et al., 2023, Zhang et al., 16 May 2025).
• Clinical and research translation: High-fidelity expert benchmarks accelerate prospective clinical integration and iterative refinement of AI models for clinical readiness (Zhang et al., 16 May 2025, Lu et al., 2023).

In summary, pathologist-authored questions represent a keystone in both training and evaluating state-of-the-art computational pathology systems, underpinning accurate, reliable, and interpretable AI-driven diagnostic assistance across research, education, and clinical domains (Buckley et al., 24 Nov 2025, He et al., 2020, Zhang et al., 16 May 2025, Lu et al., 2023, Pal et al., 2023, Wu et al., 13 Aug 2024).