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

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 B₁₂ malabsorption. This complex autoimmune response, combined with non-specific clinical manifestations, generates substantial diagnostic uncertainty. This uncertainty frequently results in misdiagnosis or delayed diagnosis and, consequently, multiple adverse outcomes, including irreversible neurological complications.

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.

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.

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.