AI in Diagnostics: Imaging, Pathology, and Beyond

AI in Diagnostics: Imaging, Pathology, and Beyond

Introduction

Diagnostic medicine is one of the earliest and most impactful areas of AI adoption. From radiology and pathology to dermatology and cardiology, AI models now assist clinicians by detecting subtle findings, prioritizing cases, and even suggesting differential diagnoses. This post illustrates where AI is making a difference today, and highlights the workflow of AI-assisted diagnostics.

Radiology

Radiology has been at the forefront of AI deployment. Convolutional neural networks (CNNs) excel at image recognition tasks such as:

  • Detecting lung nodules on CT scans.
  • Identifying intracranial hemorrhages on head CT.
  • Flagging urgent chest X-rays (e.g. pneumothorax) for rapid review.

AI does not replace the radiologist but augments their workflow by triaging studies and catching subtle abnormalities.

Pathology

Digital pathology and whole-slide imaging have unlocked new uses for AI:

  • Quantifying biomarkers (e.g. HER2 expression in breast cancer).
  • Classifying cell types and detecting rare events.
  • Automating parts of tumor grading systems.

These systems improve reproducibility and efficiency, but human oversight remains essential for context and interpretation.

Dermatology and Ophthalmology

AI is also impacting specialties reliant on image interpretation:

  • Dermatology: smartphone-based classifiers distinguish malignant melanoma from benign nevi.
  • Ophthalmology: AI detects diabetic retinopathy from fundus photographs, already FDA-cleared for autonomous use in some settings.

AI-Assisted Diagnostic Workflow

Most AI-enabled diagnostics follow a workflow where data is acquired, processed, and presented to clinicians alongside standard results. A human-in-the-loop design ensures clinicians remain the ultimate decision-makers.

Data Acquisition AI Analysis Result Presentation Clinician Review

Clinical Implications

  • Improved sensitivity. AI can flag subtle findings a human may miss.
  • Workflow efficiency. Case triage prioritizes urgent studies.
  • Equity concerns. Models trained on limited populations may underperform in others.

Conclusion & Next Step

AI is already augmenting diagnostics across multiple specialties. While powerful, these systems require oversight, context, and validation in diverse populations. In the next post, we will explore AI in Prognosis and Risk Prediction.