---
type: source
title: "Pathology in the Artificial Intelligence Era: Guiding Innovation and Implementation to Preserve Human Insight"
author: "Academic Pathology Journal (PMC11919318)"
url: https://pmc.ncbi.nlm.nih.gov/articles/PMC11919318/
date: 2025
domain: health
secondary_domains: []
format: commentary
status: unprocessed
priority: medium
tags: [clinical-ai, pathology, cytology, deskilling, never-skilling, training, cervical-screening, foundational-skills]
---

## Content

This commentary in the Academic Pathology Journal addresses AI's impact on pathologist training and the preservation of diagnostic skills. Key content:

**Deskilling mechanism in pathology:**
- AI automation of "routine processes, such as initial screenings and pattern recognition in straightforward cases" reduces pathologists' direct engagement with case diversity
- This is particularly concerning in **cervical cytology screening**, where AI can handle large volumes of routine cases, reducing trainee exposure to the full spectrum of findings

**The never-skilling mechanism in pathology:**
- Cervical cytology and routine histopathology screenings are primary automation targets
- As these become automated, trainees see fewer routine cases — but routine cases are precisely where foundational pattern recognition develops
- Reduced case exposure prevents development of "diagnostic acumen necessary for independent practice"

**Key framing:**
- "Only human experts can revise the thresholds for case prioritization" — implies AI sets the scope of human review, creating a risk of humans never encountering the edge cases that challenge their training
- Problem is particularly acute because AI may perform well in aggregate but fail on rare variants — which are exactly the cases humans need exposure to in order to handle them

**Proposed mitigations:**
- Hybrid workflows: junior pathologists review AI-supported cases AND engage independently with diverse, complex cases
- Structured mentorship: experienced pathologists supervise discrepancy reviews
- The "graduated autonomy" model: baseline competence demonstrated before AI assistance increases

**Scope:** General anatomic pathology, including histopathology, cytology (cervical screening), hematology, and tumor analysis.

**Note:** No quantitative training volume reduction data cited in this paper. The 80-85% training volume reduction figure from Session 24 requires separate sourcing (likely from a different study — the extractor should search for it specifically).

## Agent Notes

**Why this matters:** This confirms the never-skilling structural mechanism in pathology specifically. The cervical cytology example is perfect: (1) AI automation is already being deployed for routine cervical screens; (2) trainees see fewer routine cases; (3) routine cases are where foundational cytology pattern recognition develops; (4) the skill deficit won't manifest until trainees become independent practitioners facing edge cases without foundational grounding.

**What surprised me:** The paper notes that "only human experts can revise the thresholds for case prioritization" — meaning AI defines what humans see. Trainees trained under an AI threshold system may never learn to set thresholds themselves. This is a meta-skill concern beyond just diagnostic skill: the ability to calibrate what's "routine" vs. "flagged" is itself a skill that AI automation may prevent from developing.

**What I expected but didn't find:** The specific 80-85% training volume reduction figure for cytology that Session 24 mentioned. This paper describes the mechanism qualitatively but has no quantitative volume data. The extractor should search specifically for this figure — it likely comes from a cytology training program assessment study.

**KB connections:**
- Supports Belief 5 (clinical AI creates novel safety risks) specifically through the never-skilling mechanism
- The "threshold calibration" concern is a novel aspect: AI doesn't just take over tasks, it defines which tasks humans encounter
- Connects to the scoping review (PMC2949820126000123) which formalizes never-skilling as a concept

**Extraction hints:**
- CLAIM: "AI-integrated cervical cytology screening reduces trainee exposure to routine cases, creating a never-skilling risk for foundational pattern recognition skills"
- The threshold calibration insight is extractable: "AI-defined case routing prevents trainees from developing the threshold-setting skill required for independent practice"
- Scope carefully: this is a commentary, not empirical research — confidence level should be experimental, not proven

**Context:** Published in Academic Pathology, the official journal of the Association of Pathology Chairs. This is a commentary/perspective, not an original research paper. No quantitative data provided.

## Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: Session 24's proposed cytology never-skilling claim
WHY ARCHIVED: Establishes the structural mechanism for never-skilling in pathology/cytology specifically. The threshold calibration insight (AI defines what humans see) is the novel addition to Session 24's framing.
EXTRACTION HINT: The 80-85% volume reduction figure from Session 24 is NOT in this paper — it needs a separate source. This paper provides the mechanism only. Extract with experimental confidence.
