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This lesson explores key terms that describe how diseases progress, their outcomes, and their distribution in populations.Diagnostic tests identify diseases and monitor treatment. These include blood and urine tests, biopsies, imaging (X-ray, MRI), and detection of infectious agents.Remission is a reduction or disappearance of symptoms.Exacerbation refers to the worsening of symptoms, such as increased wheezing during an asthma attack.A precipitating factor triggers an acute episode, while a...
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Adaptive diagnostic reasoning framework for pathology with multimodal large language models.

Yunqi Hong1, Kuei-Chun Kao1, Liam Edwards2

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This study introduces a transparent artificial intelligence (AI) framework for pathology. The AI provides evidence-linked reasoning for diagnostic auditing, enhancing trust and clinical adoption in medical AI systems.

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Area of Science:

  • Medical Diagnostics
  • Artificial Intelligence
  • Pathology

Background:

  • Clinical adoption of AI in pathology is hindered by opaque "black box" systems.
  • A framework is needed to provide transparent, evidence-linked reasoning for diagnostic auditing.

Purpose of the Study:

  • To develop a framework that transforms opaque AI models into transparent systems.
  • To generate evidence-linked reasoning for supporting diagnostic auditing in pathology.

Main Methods:

  • Utilized off-the-shelf multimodal large language models (LLMs) for active diagnostic reasoning.
  • Employed a two-phase self-learning process on breast and prostate cancer datasets without updating model weights.
  • Integrated expert feedback from pathologists to align AI-generated criteria with medical standards.

Main Results:

  • Achieved over 90% accuracy in distinguishing normal tissue from invasive carcinoma.
  • Successfully differentiated complex subtypes like ductal carcinoma in situ by identifying key histological features.
  • Computer-generated descriptions closely matched expert pathologist assessments, demonstrating high performance and adaptability across diverse tissue types.

Conclusions:

  • The framework offers a promising approach for clinically trustworthy artificial intelligence by uniting visual understanding with reasoning.
  • This bridges the gap between opaque classifiers and auditable systems, paving the way for evidence-linked interpretation in medical workflows.