20  Pathologist AI Adoption Profiling

20.1 Introduction

While aggregate statistics show AI improves interobserver agreement, individual pathologists demonstrate heterogeneous adoption patterns. Understanding these patterns is critical for:

Note for Pathologist: We know not everyone uses AI the same way. Some people trust it completely (“High Adopters”), others are skeptical (“Conservative”). This section groups pathologists into these categories to show how different user styles affect the results. It’s not about “Good vs Bad” pathologists, but “High vs Low” AI utilization.

1. Personalized training: Identifying which pathologists benefit most from AI for which markers
   
2. Quality assurance: Detecting over-reliance or under-utilization of AI suggestions
   
3. Implementation strategies: Tailoring AI rollout based on user profiles

This chapter provides comprehensive individual-level analysis of pathologist AI adoption, moving beyond the aggregate polar plot (Figure 5 in manuscript) to formal statistical classification.

20.2 AI Adoption Indices

20.2.1 Correlation-Based Adoption Metrics

First, replicate the polar plot (Figure 5) data: correlation between Pre-AI and Post-AI assessments by pathologist and marker.

AI Adoption Index: Pre-AI vs Post-AI Correlation by Pathologist

Pathologist	ER	PR	Ki-67	HER2
Pathologist 1	0.982	0.970	0.901	0.884
Pathologist 2	0.987	0.975	0.961	0.763
Pathologist 3	0.975	0.975	0.940	0.871
Pathologist 4	0.996	0.992	0.955	0.931

20.2.1.1 Interpretation

High correlation (r > 0.90): Pathologist maintains pre-AI assessments; minimal AI influence

Moderate correlation (r = 0.70-0.90): Selective AI adoption; modifies some cases

Low correlation (r < 0.70): Substantial AI influence; frequent modifications

20.2.2 Change Magnitude Index

Quantify absolute magnitude of changes made after viewing AI suggestions.

Change Magnitude: Mean Absolute Change and % Cases Modified

pathologist	er_mean_abs_change	ki67_mean_abs_change	pr_mean_abs_change	er_pct_changed	ki67_pct_changed	pr_pct_changed
Pathologist 1	4.26	9.31	5.63	21.23	57.39	36.21
Pathologist 2	3.46	4.99	4.60	14.29	32.99	26.41
Pathologist 3	3.64	6.52	4.18	21.84	42.96	28.62
Pathologist 4	0.96	6.71	2.04	5.41	48.63	16.27

20.3 Direction Consistency Index

For Ki-67 (where systematic bias observed), assess if pathologists consistently move in same direction as AI or show variable patterns.

20.4 Latent Profile Analysis

20.4.1 Methodology

Use k-means clustering to classify pathologists into adoption archetypes based on:

Statistical caveat: With only N=4 pathologists, cluster assignments should be interpreted as descriptive characterizations rather than statistically robust classifications. K-means cannot reliably partition 4 data points; the resulting archetypes are illustrative of observed behavioral patterns, not formal statistical categories.

1. Correlation with Pre-AI (retention of initial assessment)
   
2. Mean absolute change magnitude (willingness to modify)
   
3. Variance of changes (consistency)
   
4. Marker-specific patterns (ER, PR, Ki-67, HER2)

20.4.2 Feature Engineering

Pathologist Feature Matrix for Clustering

pathologist	er_correlation	ki67_correlation	pr_correlation	er_mean_abs_change	ki67_mean_abs_change	pr_mean_abs_change	er_sd_change	ki67_sd_change	pr_sd_change	er_mean_change	ki67_mean_change	pr_mean_change	her2_correlation
Pathologist 1	0.982	0.901	0.970	4.264	9.306	5.628	7.110	9.563	9.283	-1.682	8.302	-1.400	0.884
Pathologist 2	0.988	0.960	0.976	3.462	4.990	4.595	5.957	6.136	8.239	-1.422	3.399	-1.968	0.763
Pathologist 3	0.974	0.941	0.975	3.642	6.515	4.176	7.991	7.060	7.753	0.515	5.766	-1.066	0.871
Pathologist 4	0.996	0.955	0.992	0.956	6.705	2.037	3.323	6.698	4.629	0.044	6.048	-0.051	0.931

20.4.3 Optimal Number of Clusters

Use elbow method and silhouette analysis to determine optimal k.

20.4.4 Cluster Assignments

Perform k-means with k=2 (conservative/adopter) and k=3 (conservative/moderate/adopter).

Two-Cluster Solution: Pathologist Profiles

Cluster	N Pathologists	Mean Correlation	Mean Abs Change	Profile Label
1	3	0.932	5.175	Conservative (High Retention)
2	1	0.969	3.233	Conservative (High Retention)

Pathologist Cluster Assignments (k=2)

Pathologist	Cluster	Profile
Pathologist 1	1	Conservative (High Retention)
Pathologist 2	1	Conservative (High Retention)
Pathologist 3	1	Conservative (High Retention)
Pathologist 4	2	Conservative (High Retention)

20.4.5 Three-Cluster Solution

Three-Cluster Solution: Pathologist Profiles

Cluster	N Pathologists	Mean Correlation	Mean Abs Change	Mean Ki-67 Δ	Profile Label
2	1	0.969	3.233	6.048	Conservative
3	1	0.934	6.399	8.302	Conservative
1	2	0.931	4.563	4.583	Conservative

Pathologist Cluster Assignments (k=3)

Pathologist	Cluster	Profile
Pathologist 1	3	Conservative
Pathologist 2	1	Conservative
Pathologist 3	1	Conservative
Pathologist 4	2	Conservative

20.4.6 Cluster Visualization

20.5 AI Override Rate

Definition: Percentage of cases where pathologist’s Post-AI assessment differs from their Pre-AI assessment by a clinically meaningful threshold.

20.5.1 Continuous Markers (ER, PR, Ki-67)

Threshold: >5 percentage points change

AI Override Rates (%) by Pathologist and Marker

Pathologist	ER	Ki-67	PR
Pathologist 1	21.2	57.4	36.2
Pathologist 2	14.3	33.0	26.4
Pathologist 3	21.8	43.0	28.6
Pathologist 4	5.4	48.6	16.3

20.5.2 Categorical Marker (HER2)

Override = any change in HER2 score (0 → 1+, 1+ → 2+, etc.)

HER2 Override Rates by Pathologist

Pathologist	N Cases	N Overridden	Override Rate (%)
Pathologist 1	269	22	8.2
Pathologist 2	290	45	15.5
Pathologist 3	259	21	8.1
Pathologist 4	255	11	4.3

20.5.2.1 Interpretation

High override rate (>30%): Frequent AI-driven modifications; may indicate:
- High trust in AI
- Uncertainty in initial assessments
- AI addressing genuine errors

Low override rate (<10%): Minimal AI influence; may indicate:
- High confidence in initial assessments
- Skepticism of AI suggestions
- High accuracy even without AI

20.6 Within-Pathologist Consistency

20.6.1 Variance Analysis

Compare variance of assessments Pre-AI vs Post-AI for each pathologist.

Hypothesis: If AI standardizes assessments, Post-AI variance should be lower.

Within-Pathologist Variance: Pre-AI vs Post-AI

Pathologist	Marker	Var (Pre)	Var (Post)	% Change
Pathologist 1	er	1386.44	1358.14	-2.04
Pathologist 1	ki67	429.89	481.50	12.01
Pathologist 1	pr	1408.34	1222.06	-13.23
Pathologist 2	er	1451.70	1429.62	-1.52
Pathologist 2	ki67	466.38	481.61	3.27
Pathologist 2	pr	1390.44	1272.79	-8.46
Pathologist 3	er	1209.40	1227.36	1.48
Pathologist 3	ki67	409.16	433.81	6.02
Pathologist 3	pr	1195.47	1127.21	-5.71
Pathologist 4	er	1403.43	1376.34	-1.93
Pathologist 4	ki67	458.74	514.03	12.05
Pathologist 4	pr	1366.97	1320.48	-3.40

20.6.2 Agreement with Group Median

Which pathologist is closest to group consensus (median)?

Mean Absolute Deviation from Group Median

Pathologist	Marker	Deviation (Pre-AI)	Deviation (Post-AI)
Pathologist 1	er	1.72	2.45
Pathologist 1	ki67	2.97	2.13
Pathologist 1	pr	1.41	3.17
Pathologist 2	er	2.12	3.43
Pathologist 2	ki67	3.11	4.10
Pathologist 2	pr	2.02	4.07
Pathologist 3	er	3.83	5.86
Pathologist 3	ki67	3.26	2.24
Pathologist 3	pr	2.49	5.13
Pathologist 4	er	1.87	2.26
Pathologist 4	ki67	2.27	1.80
Pathologist 4	pr	2.52	3.03

Convergence Toward Group Median After AI

Pathologist	N Markers Converged (of 3)	Mean Convergence Magnitude
Pathologist 2	3	1.45
Pathologist 3	2	1.21
Pathologist 1	2	0.55
Pathologist 4	2	0.14

20.7 Marker-Specific Adoption Patterns

20.7.1 Heatmap: Pathologist × Marker AI Adoption

20.7.2 Specialized Profiling

Identify “marker specialists”: pathologists who adopt AI differentially for specific markers.

Marker Specialization: Differential AI Adoption Patterns

Pathologist	Variance	Range	Highest AI Adoption	Lowest AI Adoption
Pathologist 2	0.011	0.225	her2	er
Pathologist 1	0.002	0.098	her2	er
Pathologist 3	0.002	0.104	her2	pr
Pathologist 4	0.001	0.065	her2	er

Interpretation:

• High variance: Pathologist adopts AI selectively for specific markers (e.g., uses AI heavily for Ki-67 but not ER)
  
• Low variance: Uniform adoption pattern across all markers

20.8 AI Adoption by Experience Level

Wu et al. (2023) found that junior pathologists benefited most from AI assistance, showing greater score modifications (Wu et al. 2023). We examine whether this pattern holds in our cohort.

Assumption: Pathologist 1 is the most senior and Pathologist 4 is the most junior (P1 → P2 → P3 → P4).

AI Adoption Index by Experience Level
Higher adoption index = more AI influence on assessments
Pathologist	Seniority	Adoption Index (1−r)	Mean |Δ|
Pathologist 1	Most Senior	0.066	6.399
Pathologist 2	Senior	0.078	4.349
Pathologist 3	Junior	0.060	4.778
Pathologist 4	Most Junior	0.031	3.233

20.8.1 Seniority–Adoption Trend

20.8.2 Seniority in Adoption Heatmap

20.9 Summary: Pathologist Archetypes

20.9.1 Final Classification

Based on clustering, override rates, consistency metrics, and seniority:

Comprehensive Pathologist AI Adoption Profiles

Pathologist	Cluster	Profile	Mean Override Rate (%)	Convergence to Median	Marker Specialization (Var)	Seniority
Pathologist 1	3	Conservative	38.28	0.55	0.00	Most Senior
Pathologist 2	1	Conservative	24.56	1.45	0.01	Senior
Pathologist 3	1	Conservative	31.14	1.21	0.00	Junior
Pathologist 4	2	Conservative	23.44	0.14	0.00	Most Junior

20.9.2 Archetype Descriptions

Pathologist Archetype Descriptions and Recommendations

Profile	Characteristics	AI Relationship	Clinical Recommendations
Conservative	High retention of Pre-AI assessments, low override rates, minimal variance change	Selective AI use; trusts initial judgment	Review AI suggestions for outlier cases; may benefit from AI confidence scores
Moderate Adopter	Balanced override rates, moderate correlation, convergence toward group median	Pragmatic AI integration; considers AI as second opinion	Current approach optimal; continue monitoring quality metrics
High Adopter	Frequent modifications, low Pre-Post correlation, substantial variance reduction	High reliance on AI suggestions	Ensure critical thinking maintained; review cases where AI differs from initial assessment

20.10 Clinical Implications

20.10.1 For Training Programs

1. Conservative pathologists: May benefit from targeted training on AI interpretation, confidence calibration
2. High adopters: May need emphasis on maintaining independent judgment, recognizing AI limitations
3. Moderate adopters: Appear to balance AI as decision support tool optimally
4. Seniority-aware training: Wu et al. (2023) found that junior pathologists benefited most from AI (Wu et al. 2023). Our data can inform whether AI should be deployed as a training tool for junior pathologists or as a quality check for senior pathologists

20.10.2 For Quality Assurance

• Monitor override rates: Sudden changes may indicate over-reliance or distrust
• Track convergence: Pathologists diverging from median warrant case review
• Marker-specific profiling: Tailor AI confidence displays based on pathologist’s marker expertise
• Experience-adjusted benchmarks: Override rate expectations may differ by seniority — higher rates for junior pathologists may be appropriate and expected

20.10.3 For AI System Design

• Personalized confidence thresholds: Show AI suggestions only when confidence exceeds pathologist-specific threshold
• Differential presentation: For “conservative” users, highlight discrepancies; for “high adopters”, emphasize agreement
• Audit trails: Flag cases where high adopters override significantly without review time
• Seniority-adaptive interface: Consider showing more AI detail (confidence intervals, supporting evidence) for junior pathologists and summarized results for experienced pathologists

Wu, Si, Meng Yue, Jun Zhang, Xiaoxian Li, Zaibo Li, Huina Zhang, Xinran Wang, et al. 2023. “The Role of Artificial Intelligence in Accurate Interpretation of HER2 Immunohistochemical Scores 0 and 1+ in Breast Cancer.” Modern Pathology 36 (3): 100054. https://doi.org/10.1016/j.modpat.2022.100054.