16  Pathology and Laboratory Medicine

TipLearning Objectives

Pathology is inherently visual pattern recognition—ideal for computer vision AI. From histopathology to hematology to clinical chemistry, AI promises to enhance diagnostic accuracy and efficiency. This chapter examines evidence-based AI applications in laboratory medicine. You will learn to:

  • Evaluate AI systems for digital pathology and histopathology interpretation
  • Understand AI applications in hematopathology and clinical lab diagnostics
  • Assess AI tools for workflow optimization and quality control
  • Navigate regulatory landscape for laboratory AI
  • Recognize limitations and failure modes specific to pathology AI
  • Apply evidence-based frameworks for pathology AI adoption

Essential for pathologists, laboratory directors, clinical laboratory scientists, and laboratory medicine teams.

The Clinical Context: Pathology may be the medical specialty most transformed by AI. Digital pathology enables whole-slide imaging analysis, and decades of archived specimens provide training data. FDA has cleared multiple AI-based pathology devices, and adoption is accelerating.

Key Applications:

16.0.1 1. Histopathology AI

✅ Prostate Cancer Detection and Grading: - FDA-cleared Paige Prostate system - Sensitivity 98% for clinically significant cancer - Reduces Gleason grading variability (Pantanowitz et al. 2020) - Published in Archives of Pathology (Pantanowitz et al. 2020)

✅ Breast Cancer Pathology: - HER2 scoring, Ki-67 quantification - Lymph node metastasis detection - FDA-cleared systems available (steiner2018impact?)

✅ Verdict: Multiple FDA-cleared systems. Pathology AI most mature specialty application.

16.0.2 2. Cytopathology AI

✅ Cervical Cytology: - Automated Pap smear analysis - Reduces false negatives by 10-20% - FDA-cleared systems (bao2023artificial?)

✅ Non-Gynecologic Cytology: - Thyroid FNA, respiratory cytology - Research stage, promising results

16.0.3 3. Hematopathology AI

⚠️ Peripheral Blood Smear Analysis: - Automated differential counts - Blast detection in leukemia - Parasitemia quantification (malaria) - Variable performance across analyzers (saba2023digital?)

⚠️ Bone Marrow Analysis: - Blast counting, cellular morphology - Research stage, not yet FDA-cleared

16.0.4 4. Clinical Laboratory AI

⚠️ Automated Result Interpretation: - Critical value detection and alerting (already standard) - Delta check automation - Quality control monitoring

✅ Microbiology AI: - Automated colony counting and identification - Antibiotic susceptibility prediction from genomics - Blood culture positivity prediction (barker2023machine?)

16.0.5 5. Workflow Optimization

✅ Case Triage and Prioritization: - AI flags high-risk cases for expedited review - Reduces turnaround time for critical diagnoses - Improves workflow efficiency (hanna2022whole?)

Challenges:

  • Standardization: Scanner variability, staining differences affect AI performance
  • Rare Diagnoses: Limited training data for uncommon pathologies
  • Medicolegal: Liability when AI misses diagnosis
  • Integration: PACS and LIS integration required

Clinical Bottom Line: Pathology AI is the most clinically mature AI application in medicine, with multiple FDA-cleared devices and growing evidence base. Pathologists should embrace AI as augmentation tool while maintaining final diagnostic responsibility.

16.1 References