Explainable Artificial Intelligence In Oral And Maxillofacial Radiology: From Black-Box Algorithms To Clinically Trustworthy Imaging
Özet
This chapter provides a comprehensive overview of Explainable Artificial Intelligence (XAI) in oral and maxillofacial radiology, highlighting its growing role in enhancing the transparency, interpretability, and clinical reliability of artificial intelligence–based imaging systems. The chapter first introduces the increasing integration of artificial intelligence into cone beam computed tomography (CBCT) and other dentomaxillofacial imaging applications, followed by a discussion of the limitations of conventional "black-box" deep learning models. Fundamental concepts of machine learning, deep learning, and convolutional neural networks are presented before exploring the principles of explainable AI, including interpretability, transparency, trustworthiness, and accountability. Widely used XAI techniques such as Grad-CAM, SHAP, saliency maps, attention maps, and LIME are reviewed with emphasis on their ability to improve clinical understanding of AI-generated decisions. The chapter further examines current clinical applications of XAI in airway analysis, CBCT segmentation, temporomandibular joint assessment, periapical lesion detection, mandibular canal localization, and orthodontic imaging. Ethical and medicolegal considerations, including clinician oversight, patient safety, algorithmic bias, and regulatory perspectives, are also discussed. Finally, future directions focusing on trustworthy AI, personalized radiology, explainable deep learning, and human–AI collaboration are presented as essential components for the safe and effective integration of artificial intelligence into routine oral and maxillofacial radiology practice.
Referanslar
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.
Negrete D, Lopes SLPdC, Barretto MddA, et al. Artificial intelligence and dentomaxillofacial radiology education: innovations and perspectives. Dentistry Journal. 2025;13(6):245.
Schwendicke Fa, Samek W, Krois J. Artificial intelligence in dentistry: chances and challenges. Journal of dental research. 2020;99(7):769–74.
Scarfe WC, Farman AG. What is cone-beam CT and how does it work? Dental Clinics of North America. 2008;52(4):707–30.
Miracle A, Mukherji S. Conebeam CT of the head and neck, part 1: physical principles. American Journal of Neuroradiology. 2009;30(6):1088–95.
White SC, Pharoah MJ. Oral radiology-E-Book: Principles and interpretation. Elsevier Health Sciences; 2014.
Mureșanu S, Almășan O, Hedeșiu M, et al. Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review. Oral Radiology. 2023;39(1):18–40.
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Medical image analysis. 2017;42:60–88.
Ali M, Irfan M, Ali T, et al. Artificial intelligence in dental radiology: a narrative review. Annals of Medicine and Surgery. 2025;87(4):2212–7.
Ariji Y, Yanashita Y, Kutsuna S, et al. Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique. Oral surgery, oral medicine, oral pathology and oral radiology. 2019;128(4):424–30.
Jacob ESoRcmoNEdSNBABAABCDCFV. What the radiologist should know about artificial intelligence–an ESR white paper. Insights into imaging. 2019;10(1):44.
Geis JR, Brady AP, Wu CC, et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Radiology. 2019;293(2):436–40.
Sarwar S, Jabin S. AI techniques for cone beam computed tomography in dentistry: trends and practices. 2023 International Conference on Recent Advances in Electrical, Electronics & Digital Healthcare Technologies (REEDCON), 2023.
Castelvecchi D. Can we open the black box of AI? Nature News. 2016;538(7623):20.
Samek W, Müller K-R. Towards explainable artificial intelligence. Explainable AI: interpreting, explaining and visualizing deep learning. Springer. 2019:5–22.
Kelly CJ, Karthikesalingam A, Suleyman M, et al. Key challenges for delivering clinical impact with artificial intelligence. BMC medicine. 2019;17(1):195.
Chartrand G, Cheng PM, Vorontsov E, et al. Deep learning: a primer for radiologists. Radiographics. 2017;37(7):2113–31.
De Vries BM, Zwezerijnen GJ, Burchell GL, et al. Explainable artificial intelligence (XAI) in radiology and nuclear medicine: a literature review. Frontiers in medicine. 2023;10:1180773.
Selvaraju RR, Cogswell M, Das A, et al. Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE international conference on computer vision, 2017.
Holzinger A, Biemann C, Pattichis CS, et al. What do we need to build explainable AI systems for the medical domain? arXiv preprint arXiv:171209923. 2017.
Mitchell TM. Artificial neural networks. Machine learning. 1997;45(81):127.
Erickson BJ, Korfiatis P, Akkus Z, et al. Machine learning for medical imaging. Radiographics. 2017;37(2):505–15.
Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–30.
Goodfellow I, Bengio Y, Courville A, et al. Deep learning: MIT press Cambridge. 2016.
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Communications of the ACM. 2017;60(6):84–90.
Doshi-Velez F, Kim B. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:170208608. 2017.
Adadi A, Berrada M. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access. 2018;6:52138–60.
Guidotti R, Monreale A, Ruggieri S, et al. A survey of methods for explaining black box models. ACM computing surveys (CSUR). 2018;51(5):1–42.
Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. Advances in neural information processing systems. 2017;30.
Ai H. High-level expert group on artificial intelligence. Ethics guidelines for trustworthy AI. 2019;6(15.3):2024.
Ahmad MA, Eckert C, Teredesai A, editors. Interpretable machine learning in healthcare. Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics, 2018.
Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nature machine intelligence. 2019;1(9):389–99.
Morley J, Machado CC, Burr C, et al. The ethics of AI in health care: a mapping review. Social science & medicine. 2020;260:113172.
Arrieta AB, Díaz-Rodríguez N, Del Ser J, et al. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion. 2020;58:82–115.
Chattopadhay A, Sarkar A, Howlader P, et al. Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. 2018 IEEE winter conference on applications of computer vision (WACV), 2018.
Molnar C, Casalicchio G, Bischl B, et al. Interpretable machine learning–a brief history, state-of-the-art and challenges. Joint European conference on machine learning and knowledge discovery in databases, 2020.
Simonyan K, Vedaldi A, Zisserman A. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:13126034. 2013.
Adebayo J, Gilmer J, Muelly M, et al. Sanity checks for saliency maps. Advances in neural information processing systems. 2018;31.
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Advances in neural information processing systems. 2017;30.
Jetley S, Lord NA, Lee N, et al. Learn to pay attention. ICLR. 2018.
Ribeiro MT, Singh S, Guestrin C, et al." Why should i trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 2016.
Carvalho DV, Pereira EM, Cardoso JS. Machine learning interpretability: A survey on methods and metrics. Electronics. 2019;8(8):832.
Sundararajan M, Taly A, Yan Q, et al. Axiomatic attribution for deep networks. International conference on machine learning, 2017.
Bach S, Binder A, Montavon G, et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS one. 2015;10(7):e0130140.
Borys K, Schmitt YA, Nauta M, et al. Explainable AI in medical imaging: An overview for clinical practitioners–Beyond saliency-based XAI approaches. European journal of radiology. 2023;162:110786.
Hung KF, Ai QYH, Wong LM, et al. Current applications of deep learning and radiomics on CT and CBCT for maxillofacial diseases. Diagnostics. 2022;13(1):110.
Tjoa E, Guan C. A survey on explainable artificial intelligence (xai): Toward medical xai. IEEE transactions on neural networks and learning systems. 2020;32(11):4793–813.
Ezhov M, Gusarev M, Golitsyna M, et al. Clinically applicable artificial intelligence system for dental diagnosis with CBCT. Scientific reports. 2021;11(1):15006.
Endres MG, Hillen F, Salloumis M, et al. Development of a deep learning algorithm for periapical disease detection in dental radiographs. Diagnostics. 2020;10(6):430.
Kwak GH, Kwak E-J, Song JM, et al. Automatic mandibular canal detection using a deep convolutional neural network. Scientific Reports. 2020;10(1):5711.
Kempers S, van Lierop P, Hsu T-MH, et al. Positional assessment of lower third molar and mandibular canal using explainable artificial intelligence. Journal of Dentistry. 2023;133:104519.
Kunz F, Stellzig-Eisenhauer A, Zeman F, et al. Artificial intelligence in orthodontics: Evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. Journal of Orofacial Orthopedics/Fortschritte der Kieferorthopädie. 2020;81(1):52–68.
Price WN, Cohen IG. Privacy in the age of medical big data. Nature medicine. 2019;25(1):37–43.
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine. 2019;25(1):44–56.
Holzinger A. Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain informatics. 2016;3(2):119–31.
Food U, Administration D. Artificial intelligence and machine learning (AI/ML)-enabled medical devices. 2024.
Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ digital medicine. 2020;3(1):118.
Esteva A, Robicquet A, Ramsundar B, et al. A guide to deep learning in healthcare. Nature medicine. 2019;25(1):24–9.
Floridi L, Cowls J. A unified framework of five principles for AI in society. Machine learning and the city: Applications in architecture and urban design. 2022:535–45.
Wiens J, Saria S, Sendak M, et al. Do no harm: a roadmap for responsible machine learning for health care. Nature medicine. 2019;25(9):1337–40.
Schork NJ. Artificial intelligence and personalized medicine. Precision medicine in Cancer therapy: Springer. 2019:265–83.
Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. The lancet digital health. 2021;3(11):e745–e50.
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology. 2017;2(4).
Longoni C, Bonezzi A, Morewedge CK. Resistance to medical artificial intelligence. Journal of consumer research. 2019;46(4):629–50.
