ISPAD, bridging clinical expertise and AI for autoimmune-related pernicious anemia diagnosis.

Background: Pernicious anemia (PA) is a severe clinical consequence of autoimmune gastritis. It results from immune-mediated damage to gastric parietal cells in the oxyntic mucosa. This process leads to intrinsic factor deficiency and subsequent vitamin B Methods: To address the intrinsic diagnostic uncertainty of PA-arising from heterogeneous, graded, and often discordant clinical, histological, immunological, and biochemical information-we developed ISPAD (Intelligent System for Pernicious Anemia Diagnosis), an explainable AI-based probabilistic framework to integrate this information, producing a probability estimate of pernicious anemia that reflects clinical reasoning rather than rigid diagnostic thresholds. Results: ISPAD was examined using a series of published diagnostically challenging cases reflecting real-world complexity. These cases included antibody assay interference, hemolysis-masked macrocytosis, seronegative presentations, and cancer-associated atrophy. Across cases, the system generated a continuous and adaptable probabilistic assessment of pernicious anemia. This assessment relied on dynamic, context-dependent integration of available information and illustrated the potential for formalizing complex diagnostic reasoning. Conclusion: ISPAD illustrates how explainable artificial intelligence can formalize expert reasoning in autoimmune-related pernicious anemia. By integrating heterogeneous and often discordant information into a transparent probabilistic framework, this proof-of-concept approach provides a structured approach to diagnostic reasoning, particularly in complex or atypical situations.