A survey of explainable AI applications in infectious disease diagnosis.
6 affiliations
- 1School of Electrical Engineering and Computer Science, Washington State University, P.O. Box 642752, Pullman, WA, 99164, USA.
- 2School of Computer Science, Amirkabir University of Technology, 350 Hafez Ave, Tehran, 1591634311, Iran.
- 3Department of Veterinary Microbiology and Pathology, Washington State University, 100 Dairy Rd, Pullman, WA, 99164, USA.
- 4School of Electrical Engineering and Computer Science, Washington State University, P.O. Box 642752, Pullman, WA, 99164, USA. [email protected].
- 5Department of Veterinary Microbiology and Pathology, Washington State University, 100 Dairy Rd, Pullman, WA, 99164, USA. [email protected].
- 6Paul G. Allen School for Global Health, Washington State University, 1155 NE College Ave, Pullman, WA, 99164, USA. [email protected].
Abstract
Artificial intelligence (AI), particularly deep learning, has shown significant promise in accelerating the diagnosis of infectious diseases. However, the inherent "black box" nature of deep learning is a major barrier to clinical adoption, preventing trust and accountability. Explainable AI (XAI) tries to address this problem by adding transparency to model decision-making. This paper presents a comprehensive scoping review of XAI applications in infectious disease diagnosis based on literature published between 2023 and 2025. We review studies of major diseases, including COVID-19, tuberculosis, malaria, monkeypox, and dengue, categorizing them by data modality (e.g., imaging, tabular) and the XAI techniques employed (e.g., SHAP, LIME, Grad-CAM). We conclude that XAI methods are effective in identifying clinically relevant features, such as key biomarkers from lab results and pathological regions in medical images, thereby aligning model reasoning with established clinical knowledge. Despite this progress, significant challenges persist, including poor generalizability from small, single-center datasets, the computational overhead of post-hoc methods, and a gap between technical explanations and clinical utility. We outline future directions that should be taken, including the need for standardized evaluation metrics, multi-center validation, and clinician-centered XAI design to facilitate the responsible integration of AI into clinical practice.Clinical trial numberNot applicable.
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BibTeX
@article{valishirin2026survey,
title = {{A survey of explainable AI applications in infectious disease diagnosis.}},
author = {Hossein Valishirin and Fatemeh Inanloo S A and Kelly A Brayton and Shira L Broschat},
journal = {BMC infectious diseases},
year = {2026},
month = jul,
doi = {10.1186/s12879-026-14018-7},
url = {https://europepmc.org/article/MED/42902181},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - A survey of explainable AI applications in infectious disease diagnosis.
AU - Hossein Valishirin
AU - Fatemeh Inanloo S A
AU - Kelly A Brayton
AU - Shira L Broschat
PY - 2026
DA - 2026/07/01
JO - BMC infectious diseases
DO - 10.1186/s12879-026-14018-7
UR - https://europepmc.org/article/MED/42902181
AB - Artificial intelligence (AI), particularly deep learning, has shown significant promise in accelerating the diagnosis of infectious diseases. However, the inherent "black box" nature of deep learning is a major barrier to clinical adoption, preventing trust and accountability. Explainable AI (XAI) tries to address this problem by adding transparency to model decision-making. This paper presents a comprehensive scoping review of XAI applications in infectious disease diagnosis based on literature published between 2023 and 2025. We review studies of major diseases, including COVID-19, tuberculosis, malaria, monkeypox, and dengue, categorizing them by data modality (e.g., imaging, tabular) and the XAI techniques employed (e.g., SHAP, LIME, Grad-CAM). We conclude that XAI methods are effective in identifying clinically relevant features, such as key biomarkers from lab results and pathological regions in medical images, thereby aligning model reasoning with established clinical knowledge. Despite this progress, significant challenges persist, including poor generalizability from small, single-center datasets, the computational overhead of post-hoc methods, and a gap between technical explanations and clinical utility. We outline future directions that should be taken, including the need for standardized evaluation metrics, multi-center validation, and clinician-centered XAI design to facilitate the responsible integration of AI into clinical practice.Clinical trial numberNot applicable.
SN - 1471-2334
ER -