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The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.

Explainable Artificial Intelligence for Tabular Data in Healthcare: A Systematic Review of Methods, Evaluation, and Applications

Santana-Velásquez, Angelower, Salazar-Sánchez, Maria Bernarda·2026·Computers
DOI 10.3390/computers15080506
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Abstract

Explainable Artificial Intelligence (XAI) has emerged as a critical enabler for the adoption of machine learning models in high-stakes domains such as healthcare. While significant progress has been made in XAI for computer vision and natural language processing, tabular data—the predominant format of electronic health records—presents unique challenges and opportunities. This systematic review provides a comprehensive analysis of XAI methods specifically applied to tabular healthcare data for classification tasks. We examine 21 primary studies published between 2020 and the first half of 2026, covering three complementary perspectives: (1) intrinsically interpretable models, (2) post-hoc methods including LIME, SHAP, and their variants, and (3) evaluation frameworks that assess both model-centered fidelity and human-centered clinical alignment. Our analysis reveals that SHAP remains the dominant post-hoc method, achieving strong model fidelity but showing inconsistent alignment with clinical expert reasoning. Key findings include the significant impact of class imbalance on explanation consistency, the importance of clinician-centered evaluation, and the emergence of hybrid approaches integrating XAI with generative AI and transfer learning. We identify critical gaps, including limited adoption of XAI in AutoML pipelines, lack of standardized evaluation metrics, and predominance of single-institution validation studies.

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Cites

Research works printed but not linked: 11

  1. Doshi-Velez, F., and Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv.
  2. Lundberg, S., and Lee, S. (2017, January 4–9). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA.
  3. Mubarakali, 2025, IoT and XAI-driven data aggregation framework for intelligent decision-making in smart healthcare systems, Comput. Electr. Eng., 48, 101179
  4. Rai, T., He, J., Kaur, J., Shen, Y., Mahmud, M., Brown, D.J., O’Dowd, E., and Baldwin, D.R. (2025, January 26). Evaluating LIME and SHAP Under Class Imbalance: A Lung Cancer Prediction Case Study Using CPRD Data. Proceedings of the 4th International Workshop, XAI-Healthcare 2025, and 2nd International Workshop, PM4H 2025, Pavia, Italy.
  5. Ahmed, 2025, Automated stress detection with explainable-AI transformer TabNet technique and physiological signals, Biomed. Signal Process. Control, 109, 109208
  6. Thomas, J., and Brown, S. (2025, January 19–20). Explainable AI for Osteoporosis Risk Modeling: Random Forest and SHAP Interpretation on CTGAN-Enhanced Data. Proceedings of the 2025 IEEE APCIT, Mysore, India.
  7. Vidhya, K., Surya, S.K., Geetha, S.K., Thiyagu, T.M., Catherine Joy, R., and Archpaul, J. (2025, January 4–6). TabNet Fusion Framework for Explainable Multi-Omics Alzheimer’s Disease Classification. Proceedings of the 2025 IEEE ICICI, Bangalore, India.
  8. Rajani, B., Varsha, K., Srinivas, K., Sivamani, N., and Ahmed, K.T. (2025, January 17–19). A Robust and Explainable Machine Learning Framework for Early Lung Cancer Risk Prediction using Synthetic Data Augmentation and Cross-Dataset Validation. Proceedings of the 2025 IEEE ICSEDIS, Chiramanangad, India.
  9. Swain, 2025, Model-Agnostic Interpretability Techniques for Machine Learning-Based Pre-Term Birth Prediction, Advances in Intelligent Systems and Computing, Volume 1624, 337
  10. Apley, 2020, Visualizing the effects of predictor variables in black box supervised learning models, J. Am. Stat. Assoc., 82, 1059
  11. Mekonnen, E. (2025, January 9–11). Explaining time series classifiers through post-hoc XAI methods capturing temporal dependencies. Proceedings of the 3rd World Conference on eXplainable Artificial Intelligence (xAI 2025), Istanbul, Turkey.

Metadata

Authors
Santana-Velásquez, A., & Salazar-Sánchez, M. B.
Venue
Computers
Publisher
MDPI AG
Published
2026-08-06
Volume
15
Issue
8
Pages
506
ISSN
2073-431X
DOI
10.3390/computers15080506
Cited by
0
References
34
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Universidad de Antioquia, Colombia (CODI-PRG 2022-52993)

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