Explainable Artificial Intelligence for Tuberculosis Detection: A Comprehensive Review of Techniques, Challenges, and Future Directions
Keywords:
Deep learning, Explainable artificial intelligence (XAI), Grad-CAM, Healthcare, Interpretability, LIME, Medical imaging, SHAP, Tuberculosis detectionAbstract
Explainable artificial intelligence (XAI) has appeared as an important study domain in healthcare, resolving the constraints of black-box machine learning and deep learning frameworks. In the field of tuberculosis recognition, AI methods have proven precision in evaluating chest X-ray scans and medical data; however, the absence of explainability restricts their implementation in healthcare settings. This study shows an extensive analysis of 50 research papers emphasizing on the utilization of XAI in tuberculosis recognition. The chosen papers are evaluated based on techniques, datasets, interpretability methods and assessment parameters. Frequently applied XAI procedures, such as LIME, SHAP, and Grad-CAM, are studied in depth. The analysis shows that although these methods increase transparency, they are frequently restricted to certain kinds of descriptions, such as local-global or spatial, causing partial awareness of system performance. Additionally, this paper detects important problems, including the lack of consistent analysis parameters, restricted application of hybrid XAI systems and absence of medical confirmation. The results indicate that combining multiple XAI methods and implementing measurable assessment procedures can considerably improve the trustworthiness and reliability of AI-driven TB identification frameworks. Ultimately, upcoming study areas are described to lead the advancement of clear, understandable and medically usable AI algorithms in healthcare.
References
A. Aravindkumar, M. Ramadoss, S. A. Fakhruddin Ahmed, V. Sampath, and K. Lakshminarayanan, “Explainable AI in healthcare: a systematic review of XAI use cases in imaging, diagnostics, and rehabilitation,” Frontiers in Artificial Intelligence, vol. 9, Apr. 2026.
Md. A. B. Shiddik, “Explainable artificial intelligence in healthcare: Current landscape, challenges, and future directions,” Health Science Reports, vol. 9, no. 3, e72172, Mar. 2026.
Patrick McGonagle, William Farrelly, Kevin Curran, Explainable AI: A combined XAI framework for explaining brain tumour detection models, 05 February 2026.
Alessandro Gambetti, Qiwei Han, Hong Shen and Claudia Soares, A survey on human-centered evaluation of explainable ai methods in clinical decision support systems, 23 Jan 2026.
M. Mersha, K. Lam, J. Wood, A. K. AlShami, and J. Kalita, “Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction,” Neurocomputing, vol. 599, p. 128111, Sep. 2024.
J. Hou et al., “Self-eXplainable AI for medical image analysis: A survey and new outlooks,” arXiv (Cornell University), Oct. 2024.
B. D. Mienye, G. Obaido, N. Jere, E. Mienye, and B. Ogbuokiri, “A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges,” Informatics in Medicine Unlocked, vol. 51, Art. no. 101587, Aug. 22, 2024.
Md. S. Rahman and A. B. Shiddik, “Utilizing artificial intelligence to predict and analyze socioeconomic, environmental, and healthcare factors driving tuberculosis globally,” Scientific Reports, vol. 15, no. 1, Apr. 2025.
B. Nansamba, J. Nakatumba-Nabende, A. Katumba, and D. P. Kateete, “A systematic review on application of multimodal learning and explainable AI in tuberculosis detection,” IEEE Access, pp. 1–1, Jan. 2025.
K. Vanitha, T. R. Mahesh, V. V. Kumar, and Suresh Guluwadi, “Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays,” BMC Medical Imaging, vol. 25, no. 96, Mar. 2025.
Razan Alkhanbouli, Hour, F. Alhosani, and M. Can, “The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions,” BMC Medical Informatics and Decision Making, vol. 25, no. 1, Mar. 2025.
Z. Naz, M. U. G. Khan, T. Saba, A. Rehman, H. Nobanee, and S. A. Bahaj, “An explainable AI-enabled framework for interpreting pulmonary diseases from chest radiographs,” Cancers, vol. 15, no. 1, p. 314, Jan. 2023.
M. Laamarti, Y. El Fathi Lalaoui, R. Elfermi, R. Daoud, and A. El Allali, “Afro-TB dataset as a large-scale genomic data of Mycobacterium tuberuclosis in Africa,” Scientific Data, vol. 10, no. 1, Apr. 2023.
T. T. H. Nguyen, V. B. Truong, V. T. K. Nguyen, Q. H. Cao, and Q. K. Nguyen, “Towards Trust of Explainable AI in Thyroid Nodule Diagnosis,” arXiv preprint, Mar. 8, 2023.
Z. Sadeghi et al., “A review of Explainable Artificial Intelligence in healthcare,” Computers & Electrical Engineering, vol. 118, no. 109370, Aug. 2024.
Kamble V. B, Halgare N. M, Mohammed Aejaz Tumkur “Explainable artificial intelligence (XAI) in healthcare: A comprehensive review” International Journal of Trend in Scientific Research and Development (IJTSRD), vol. 9, no. 6, December 2025, pp. 275-277.
R. Shankar, Z. Goh, F. Devi, and Q. Xu, “A systematic review of explainable artificial intelligence methods for speech-based cognitive decline detection,” NPJ Digital Medicine, vol. 8, no. 1, Nov. 2025.
Abdullah Nuruddin Jalgaonkar, Explainable artificial intelligence (XAI) for medical diagnosis: A comprehensive review of interpretability frameworks, Clinical Integration, and Trust Metrics, vol. 12, no. 6, Nov. 2025.
S. Bai, S. Nasir, R. A. Khan, A. Meyer, and H. Konik, “Breast cancer diagnosis with explainable artificial intelligence (XAI): Uncovering strengths and biases,” IEEE Access, vol. 13, pp. 205197–205223, 2025.
N. Ullah, J. A. Khan, I. D. Falco, and G. Sannino, “Explainable Artificial Intelligence: Importance, Use Domains, Stages, Output Shapes, and Challenges,” ACM Computing Surveys, vol. 57, no. 4, pp. 1–36, Nov. 2024.
F. Ahmed, N. S. Naz, S. Khan, A. U. Rehman, W. M. Ismael, and M. A. Khan, “Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges,” BMC Medical Imaging, vol. 26, no. 1, Jan. 2026.
B. Wen, Y. Wu, T. Daqqaq, and A. Chaddad, “Towards a transparent and interpretable AI model for medical image classifications,” Cognitive Neurodynamics, vol. 19, no. 1, Sep. 2025.
K. Borys et al., “Explainable AI in medical imaging: An overview for clinical practitioners – Saliency-based XAI approaches,” European Journal of Radiology, vol. 162, p. 110787, May 2023.
A. Carriero, K. G. Moons, and Maarten van Smeden, “Explainable AI in healthcare: to explain, to predict, or to describe?,” Diagnostic and Prognostic Research, vol. 9, no. 1, pp. 29–29, Dec. 2025.
A. Chaddad, J. Peng, J. Xu, and A. Bouridane, “Survey of explainable AI techniques in healthcare,” Sensors, vol. 23, no. 2, p. 634, Jan. 2023.
M. Bhandari, T. B. Shahi, B. Siku, and A. Neupane, “Explanatory classification of CXR images into COVID-19, pneumonia and tuberculosis using deep learning and XAI,” Computers in Biology and Medicine, vol. 150, p. 106156, Nov. 2022.
Deepshikha Bhati, Fnu Neha, and Md Amiruzzaman, “A survey on explainable artificial intelligence (XAI) techniques for visualizing deep learning models in medical imaging,” Journal of Imaging, vol. 10, no. 10, pp. 239–239, Sep. 2024.
E. Kyrimi, S. McLachlan, J. M. Wohlgemut, Z. B. Perkins, D. A. Lagnado, W. Marsh, et al., “Explainable AI: Definition and attributes of a good explanation for health AI,” arXiv preprint, Sep. 9, 2024.
D. Winster et al., “Exploring Explainable Artificial Intelligence for Transparent Decision Making,” E3S web of conferences, vol. 399, pp. 04030–04030, Jan. 2023.
S. S. Band et al., “Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods,” Informatics in Medicine Unlocked, vol. 40, pp. 101286–101286, Jan. 2023.
Jesus M. Darias, Bel´en D´ıaz-Agudo, and Juan A. RecioGarcia, A systematic review on model-agnostic XAI libraries, 2021.
A. F. Markus, J. A. Kors, and P. R. Rijnbeek, “The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies,” Journal of Biomedical Informatics, vol. 113, no. 103655, Jan. 2021.
R. Roy, M. B. Rani, D. F. Hordofa, and S. Mukherjee, “Bridging human-AI collaboration in healthcare: a systematic review of explainable AI applications,” Discover Applied Sciences, vol. 8, no. 154, Dec. 2025.
H. Eshkiki, Fabio Caraffini, and B. Mora, “a survey of the application of explainable artificial intelligence in biomedical informatics,” Applied Sciences, vol. 15, no. 24, pp. 12934–12934, Dec. 2025.
S. Hong et al., Safeguarding patient trust in the Age of AI: Tackling health misinformation with explainable AI. arXiv preprint, 4 Sep 2025.
Y. Singh et al., “Beyond post hoc explanations: A comprehensive framework for accountable AI in medical imaging through transparency, interpretability, and explainability,” Bioengineering, vol. 12, no. 8, pp. 879–879, Aug. 2025.
M. Haupt, M. H. Maurer, and R. P. Thomas, “Explainable artificial intelligence in radiological cardiovascular imaging: A systematic review,” Diagnostics (Basel, Switzerland), vol. 15, no. 11, p. 1399, 2025.
A. Saranya and R. Subhashini, “A systematic review of explainable artificial intelligence models and applications: Recent developments and future trends,” Decision Analytics Journal, vol. 7, no. 100230, Apr. 2023.
C. Özkurt, “Improving tuberculosis diagnosis using explainable artificial intelligence in medical imaging,” Journal of Mathematical Sciences and Modelling, vol. 7, no. 1, pp. 33–44, May 2024.
Luigi Palla, “Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning,” PLoS Computational Biology, vol. 20, no. 8, pp. e1012260–e1012260, Aug. 2024.
João Cálem, C. Moreira, and J. Jorge, “Intelligent systems in healthcare: A systematic survey of explainable user interfaces,” Computers in Biology and Medicine, vol. 180, pp. 108908–108908, Jul. 2024.
Muhammad Aasem and Muhammad Javed Iqbal, “Toward explainable AI in radiology: Ensemble-CAM for effective thoracic disease localization in chest X-ray images using weak supervised learning,” Frontiers in Big Data, vol. 7, May 2024.
Al Amin Biswas, “A comprehensive review of explainable AI for disease diagnosis,” Array, Apr. 2024, vol. 22, no. 100345, Apr. 2024.
A. Amin, K. Hasan, S. Zein-Sabatto, D. Chimba, I. Ahmed, and T. Islam, “An explainable AI framework for artificial intelligence of medical things,” arXiv (Cornell University), Mar. 2024.
Q. Huang, J. Fan, H. Xu, W. Han, X. Huang, and Y. Chen, “AFENet: Attention-guided feature enhancement network and a benchmark for low-altitude UAV sewage outfall detection,” Array, vol. 22, p. 100343, Jul. 2024.
M. Settino and M. Cannataro, “MMRFVariant: Prioritizing variants in multiple myeloma,” Informatics in Medicine Unlocked, vol. 39, p. 101271, 2023.
F. Di Martino and F. Delmastro, “Explainable AI for clinical and remote health applications: a survey on tabular and time series data,” Artificial Intelligence Review, Oct. 2022.
G. Arjunan, “Implementing explainable AI in healthcare: Techniques for interpretable machine learning models in clinical decision-making,” International Journal of Scientific Research and Management (IJSRM), vol. 9, no. 05, pp. 597–603, May 2021.
S. M. K. Sistla, V. Periyasamy, and J. Jeyaraman, Explainable AI techniques and applications in healthcare,” International Journal of Innovative Science and Research Technology, vol. 9, no. 2, pp. 1477–1483, Feb. 2024.
T. Hulsen, “Explainable artificial intelligence (XAI): Concepts and challenges in healthcare,” AI, vol. 4, no. 3, pp. 652–666, Aug. 2023.
