Explainable Multimodal Artificial Intelligence for Precision Laboratory Diagnosis: Integrating Biomarkers, Molecular Profiles, Immunological Signatures, and Clinical Data
Singh Aryan Joseph
Samarkand State Medical University, Samarkand, 140104, Uzbekistan.
Ajit Pal Singh
*
Department of Medical Lab Technology, School of Allied Health Sciences, Galgotias University, Greater Noida, 203201, India.
Maria Agbo-Inedu Ihotu Owoicho
Jos University Teaching Hospital, 93000, Jos, Nigeria.
*Author to whom correspondence should be addressed.
Abstract
Background: Precision laboratory diagnosis increasingly relies on the interpretation of heterogeneous biological and clinical information. Conventional workflows tend to examine biomarkers, molecular measurements, immunological findings, and clinical characteristics along partly separate analytical pathways. Artificial intelligence (AI) can uncover complex relationships in high-dimensional biomedical data, yet many such systems remain hard to interpret at the point of clinical use. Explainable artificial intelligence (XAI) renders algorithmic outputs more transparent by identifying the features, modalities, and interactions that shape a prediction.
Objective: This manuscript proposes an explainable multimodal AI framework for precision laboratory diagnosis, termed XMAI-PLD, which unites conventional laboratory biomarkers, molecular profiles, immunological signatures, and clinical data within a single analytical architecture. The framework is designed to support, rather than supplant, laboratory expertise and clinical judgement.
Framework: The architecture comprises seven interconnected layers: data acquisition; quality control and harmonisation; modality-specific representation; multimodal fusion; diagnostic inference; explainability and uncertainty assessment; and laboratory–clinical decision support. Biomarker measurements contribute quantitative biochemical information, molecular profiles characterise genomic or molecular alterations, immunological signatures capture host-response patterns, and clinical variables supply patient-level context. Each stream is processed separately before being integrated through multimodal learning, and explanations are generated at the modality and feature levels to reveal the principal drivers of each output.
Clinical and Laboratory Relevance: The framework addresses the shortcomings of isolated single-modality models, namely incomplete biological representation, heterogeneous data structures, missing information, analytical variability, and limited interpretability. Its proposed outputs comprise diagnostic probabilities, contributing features, modality-level evidence, uncertainty estimates, and cases flagged for human review. Particular emphasis falls on analytical validity, calibration, external validation, data provenance, subgroup assessment, cybersecurity, privacy, and human oversight.
Conclusion: Explainable multimodal AI offers a promising route towards a more integrated diagnostic representation of laboratory and clinical evidence. Its worth will hinge not only on predictive performance but also on analytical robustness, reproducibility, interpretability, calibration, clinical usefulness, and sustained human oversight. Prospective, multicentre evaluation is required before such systems enter routine diagnostic practice.
Keywords: Explainable artificial intelligence, multimodal artificial intelligence, precision laboratory diagnosis, biomarkers, molecular profiling, immunological signatures, clinical data integration, uncertainty estimation, human–AI interaction, clinical decision support