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Cited by 238Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Enhancing interpretability and accuracy of AI models in healthcare: a comprehensive review on challenges and future directions

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Ennab, Mohammad, Mcheick, Hamid·2024·Frontiers in Robotics and AI
DOI 10.3389/frobt.2024.1444763PMC PMC11638409
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Abstract

Artificial Intelligence (AI) has demonstrated exceptional performance in automating critical healthcare tasks, such as diagnostic imaging analysis and predictive modeling, often surpassing human capabilities. The integration of AI in healthcare promises substantial improvements in patient outcomes, including faster diagnosis and personalized treatment plans. However, AI models frequently lack interpretability, leading to significant challenges concerning their performance and generalizability across diverse patient populations. These opaque AI technologies raise serious patient safety concerns, as non-interpretable models can result in improper treatment decisions due to misinterpretations by healthcare providers. Our systematic review explores various AI applications in healthcare, focusing on the critical assessment of model interpretability and accuracy. We identify and elucidate the most significant limitations of current AI systems, such as the black-box nature of deep learning models and the variability in performance across different clinical settings. By addressing these challenges, our objective is to provide healthcare providers with well-informed strategies to develop innovative and safe AI solutions. This review aims to ensure that future AI implementations in healthcare not only enhance performance but also maintain transparency and patient safety.

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  • Traditional machine learning models often lack interpretability, making them less suited for ethically sensitive applications in mental health law.

    “Furthermore, traditional machine learning models often lack interpretability, making them less suited for ethically sensitive applications in mental health law”

    From A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support (2025) · cites this paper as Ennab and Mcheick, 2024

    The Introduction (page unavailable) describes deep-learning algorithms as often being black boxes, and §7 (page unavailable) says interpretability is especially important in high-risk healthcare settings. The paper does not extend that assessment to traditional machine-learning models generally—§7.1.1 calls a decision tree easy to interpret—nor does it assess their suitability for mental health law. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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  1. Afrash M. R. Shafiee M. Kazemi-Arpanahi H. (2023). Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors. BMC Gastroenterol. 23 (1), 6. 10.1186/s12876-022-02626-x 36627564 PMC9832798 · DOI
  2. Alaa A. Gurdasani D. Harris A. Rashbass J. Schaar M. v. d. (2020) Machine learning to guide the use of adjuvant therapies for breast cancer. 10.21203/rs.3.rs-53594/v1 · DOI
  3. Arbabshirani M. R. Fornwalt B. K. Mongelluzzo G. J. Suever J. D. Geise B. D. Patel A. A. (2018). Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. NPJ Digit. Med. 1, 9. 10.1038/s41746-017-0015-z 31304294 PMC6550144 · DOI · Paper page
  4. Archana R. Jeevaraj P. S. E. (2024). Deep learning models for digital image processing: a review. Artif. Intell. Rev. 57 (1), 11. 10.1007/s10462-023-10631-z · DOI
  5. Arora A. Faulkner J. Paleri V. Kapoor K. Al-Lami A. Olaleye O. (2024). New robotic platform for transoral robotic surgery: an IDEAL stage 0 study. BMJ Surg. interventions, & health Technol. 6 (1), e000181. 10.1136/bmjsit-2022-000181 PMC10946345 38500710 · DOI
  6. Baeza-Yates R. (2022). “ Ethical challenges in AI,” in WSDM ’22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Tempe, AZ, USA, February 21–25, 2022 (ACM), 1– 2. 10.1145/3488560.3498370 · DOI
  7. Bibi N. Ud Din I. Sikandar M. Almogren A. Ali S. (2020). IoMT-based automated detection and classification of leukemia using deep learning. J. Healthc. Eng. 6648574, 6648574. 10.1155/2020/6648574 PMC7732373 33343851 · DOI
  8. Bird D. McCallum H. Nix M. G. Teo M. Gilbert A. Casanova N. (2021). Multicentre, deep learning, synthetic-CT generation for ano-rectal MR-only radiotherapy treatment planning. Radiother. Oncol. 156 (1), 23– 28. 10.1016/j.radonc.2020.11.027 33264638 PMC8050018 · DOI
  9. Biswas P. Sikander S. Kulkarni P. (2023). Recent advances in robot-assisted surgical systems. Biomed. Eng. Adv. 6, 100109. 10.1016/j.bea.2023.100109 · DOI
  10. Bozyel S. Şimşek E. Koçyiğit Burunkaya D. Güler A. Korkmaz Y. Şeker M. (2024). Artificial intelligence-based clinical decision support systems in cardiovascular diseases. Anatol. J. Cardiol. 28 (2), 74– 86. 10.14744/AnatolJCardiol.2023.3685 38168009 PMC10837676 · DOI
  11. Bukhari M. (2022). “ A deep learning framework for leukemia cancer detection in microscopic blood samples using squeeze and excitation learning,” in Mathematical problems in engineering. Editor Ali N. 10.1155/2022/2801227 · DOI
  12. Cao S. (2024). A systematic literature review on explainability for machine/deep learning-based software engineering research. ArXiv, abs/2401.14617. · arXiv
  13. Carvalho D. V. Pereira E. M. Cardoso J. S. (2019). Machine learning interpretability: a survey on methods and metrics. Electronics 8 (8), 832. 10.3390/electronics8080832 · DOI · Paper page
  14. Chen D. Wu L. Li Y. Zhang J. Liu J. Huang L. (2020). Comparing blind spots of unsedated ultrafine, sedated, and unsedated conventional gastroscopy with and without artificial intelligence: a prospective, single-blind, 3-parallel-group, randomized, single-center trial. Gastrointest. Endosc. 91 (2), 332– 339.e3. 10.1016/j.gie.2019.09.016 31541626 · DOI
  15. Chen K. Shao A. Burapacheep J. Li Y. (2024). Conversational AI and equity through assessing GPT-3’s communication with diverse social groups on contentious topics. Sci. Rep. 14 (1), 1561. 10.1038/s41598-024-51969-w 38238474 PMC10796352 · DOI
  16. Chiou S.-Y. Zhang Z.-Y. Liu H.-L. Yan J.-L. Wei K.-C. Chen P.-Y. (2022). Augmented reality surgical navigation system for external ventricular drain. Basel, Switzerland: Healthcare 10 (10). 10.3390/healthcare10101815 PMC9601392 36292263 · DOI
  17. Data sharing in the age of deep learning (2023). Nature Biotechnology 41 (4), 433. 10.1038/s41587-023-01770-3 37020134 · DOI
  18. Dratsch T. Zäske C. Siedek F. Rauen P. Hokamp N. G. Sonnabend K. (2024). Reconstruction of 3D knee MRI using deep learning and compressed sensing: a validation study on healthy volunteers. Eur. Radiol. Exp. 8 (1), 47. 10.1186/s41747-024-00446-0 38616220 PMC11016523 · DOI
  19. Eckardt J.-N. Middeke J. M. Riechert S. Schmittmann T. Sulaiman A. S. Kramer M. (2022). Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears. Leukemia 36 (1), 111– 118. 10.1038/s41375-021-01408-w 34497326 PMC8727290 · DOI
  20. El Shawi R. (2019). “ Interpretability in HealthCare A comparative study of local machine learning interpretability techniques,” in 2019 IEEE 32nd international symposium on computer-based medical systems (CBMS), 275– 280. 10.1109/CBMS.2019.00065 · DOI
  21. Ennab M. Mcheick H. (2022a). Designing an interpretability-based model to explain the artificial intelligence algorithms in healthcare. Diagn. (Basel, Switzerland) 12 (7), 1557. 10.3390/diagnostics12071557 PMC9319389 35885463 · DOI · Paper page
  22. Ennab M. Mcheick H. (2022b). Survey of COVID-19 prediction models and their limitations. Inf. Syst. 11 (2), 14– 21. 10.11648/j.ijiis.20221102.11 · DOI
  23. Escalante H. J. Escalera S. Guyon I. Baró X. Güçlütürk Y. Güçlü U. (2018). “ Explainable and interpretable models in computer vision and machine learning,” in Springer Verlag (The Springer Series on Challenges in Machine Learning). Available at: https://inria.hal.science/hal-01991623.
  24. Esteva A Kuprel B. Novoa R. A. Ko J. Swetter S. M. Blau H. M. (2017). Erratum: corrigendum: Dermatologist-level classification of skin cancer with deep neural networks. Nature 546 (7660), 686. 10.1038/nature22985 28658222 · DOI
  25. Frisch S. Libuschewski H. Peters S. Gerß J. von Hoff K. Kortmann R. D. (2024). Radiation therapy plays an important role in the treatment of atypical teratoid/rhabdoid tumors: analysis of the EU-RHAB cohorts and their precursors. Int. J. Radiat. Oncol. Biol. Phys. 119 (4), 1147– 1157. 10.1016/j.ijrobp.2024.01.200 38246248 · DOI
  26. Fu Y. Liu R. Liu Y. Lu J. (2019). Intertrochanteric fracture visualization and analysis using a map projection technique. Med. Biol. Eng. Comput. 57 (3), 633– 642. 10.1007/s11517-018-1905-1 30311066 · DOI
  27. Fuhrman J. D. Gorre N. Hu Q. Li H. El Naqa I. Giger M. L. (2022). A review of explainable and interpretable AI with applications in COVID‐19 imaging. Med. Phys. 49 (1), 1– 14. 10.1002/mp.15359 34796530 PMC8646613 · DOI
  28. Garreau D. von Luxburg U. (2020). “ Explaining the explainer: a first theoretical analysis of LIME,” in Proceedings of the twenty third international Conference on artificial Intelligence and statistics. PMLR (proceedings of machine learning research). Editors Chiappa S. Calandra R., 1287– 1296. Available at: https://proceedings.mlr.press/v108/garreau20a.html.
  29. Gilpin L. H. (2018). “ Explaining explanations: an overview of interpretability of machine learning,” in 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA) (IEEE), 80– 89.
  30. Gottlieb M. Kline J. A. Schneider A. J. Coates W. C. (2023). ChatGPT and conversational artificial intelligence: friend, foe, or future of research? Am. J. Emerg. Med. 70, 81– 83. 10.1016/j.ajem.2023.05.018 37229893 · DOI
  31. Gumbs A. A. Frigerio I. Spolverato G. Croner R. Illanes A. Chouillard E. (2021). Artificial intelligence surgery: how do we get to autonomous actions in surgery? Sensors (Basel, Switzerland) 21 (16), 5526. 10.3390/s21165526 34450976 PMC8400539 · DOI · Paper page
  32. Guo W. (2018). Explaining deep learning models--A bayesian non-parametric approach. Adv. neural Inf. Process. Syst. 31.
  33. Hall W. A. Paulson E. Li X. A. Erickson B. Schultz C. Tree A. (2022). Magnetic resonance linear accelerator technology and adaptive radiation therapy: an overview for clinicians. CA a cancer J. Clin. 72 (1), 34– 56. 10.3322/caac.21707 PMC8985054 34792808 · DOI
  34. Hamedan F. Orooji A. Sanadgol H. Sheikhtaheri A. (2020). Clinical decision support system to predict chronic kidney disease: a fuzzy expert system approach. Int. J. Med. Inf. 138 (104134), 104134. 10.1016/j.ijmedinf.2020.104134 32298972 · DOI
  35. Han T. (2023) MedAlpaca – an open-source collection of medical conversational AI models and training data. 10.48550/arXiv.2304.08247 · DOI
  36. Hassan S. A. Akbar S. Khan H. U. (2024). Detection of central serous retinopathy using deep learning through retinal images. Multimedia Tools Appl. 83 (7), 21369– 21396. 10.1007/s11042-023-16206-y · DOI
  37. Helman S. Terry M. A. Pellathy T. Williams A. Dubrawski A. Clermont G. (2022). Engaging clinicians early during the development of a graphical user display of an intelligent alerting system at the bedside. Int. J. Med. Inform. 159, 104643. 10.1016/j.ijmedinf.2021.104643 34973608 PMC9040820 · DOI · Paper page
  38. Hooshmand S. A. Zarei Ghobadi M. Hooshmand S. E. Azimzadeh Jamalkandi S. Alavi S. M. Masoudi-Nejad A. (2021). A multimodal deep learning-based drug repurposing approach for treatment of COVID-19. Mol. Divers 25 (3), 1717– 1730. 10.1007/s11030-020-10144-9 32997257 PMC7525234 · DOI · Paper page
  39. Hossain M. M. Walid M. A. A. Galib S. S. Azad M. M. Rahman W. Shafi A. (2024). COVID-19 detection from chest CT images using optimized deep features and ensemble classification. Syst. Soft Comput. 6, 200077. 10.1016/j.sasc.2024.200077 · DOI
  40. Howard F. M. Kochanny S. Koshy M. Spiotto M. Pearson A. T. (2020). Machine learning-guided adjuvant treatment of head and neck cancer. JAMA Netw. open 3 (11), e2025881. 10.1001/jamanetworkopen.2020.25881 33211108 PMC7677764 · DOI
  41. Hu Y. (2019). “ Multi-modal probabilistic prediction of interactive behavior via an interpretable model,” in 2019 IEEE intelligent vehicles symposium (IV) (IEEE), 557– 563.
  42. Jing J. Liu Z. Guan H. Zhu W. Zhang Z. Meng X. (2023). A deep learning system to predict recurrence and disability outcomes in patients with transient ischemic attack or ischemic stroke. Adv. Intell. Syst. 5 (4), 2200240. 10.1002/aisy.202200240 · DOI
  43. Johansson U. Sönströd C. Norinder U. Boström H. (2011). Trade-off between accuracy and interpretability for predictive in silico modeling. Future Med. Chem. 3 (6), 647– 663. 10.4155/fmc.11.23 21554073 · DOI
  44. Kaushik P. (2023). Artificial intelligence accelerated transformation in the healthcare industry. Amity J. Prof. Pract. 3. 10.55054/ajpp.v3i01.630 · DOI
  45. Ke Y.-Y. Peng T.-T. Yeh T. K. Huang W. Z. Chang S. E. Wu S. H. (2020). Artificial intelligence approach fighting COVID-19 with repurposing drugs. Biomed. J. 43 (4), 355– 362. 10.1016/j.bj.2020.05.001 32426387 PMC7227517 · DOI
  46. Khalifa M. Albadawy M. Iqbal U. (2024). Advancing clinical decision support: the role of artificial intelligence across six domains. Comput. Methods Programs Biomed. Update 5, 100142. 10.1016/j.cmpbup.2024.100142 · DOI
  47. Kim B. (2017) Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV). 10.48550/ARXIV.1711.11279 · DOI · Paper page
  48. Kim B. (2019). “ Why are saliency maps noisy? Cause of and solution to noisy saliency maps,” in 2019 IEEE/CVF international conference on computer vision workshop (ICCVW), Seoul, Korea (South), October 27–28, 2019 (IEEE), 4149– 4157.
  49. Kim H. Yoo S. K. Kim J. S. Kim Y. T. Lee J. W. Kim C. (2024). Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT. Sci. Rep. 14 (1), 8504. 10.1038/s41598-024-59014-6 38605094 PMC11009270 · DOI
  50. Lee A. Baker T. S. Bederson J. B. Rapoport B. I. (2024). Levels of autonomy in FDA-cleared surgical robots: a systematic review. npj Digit. Med. 7 (1), 103. 10.1038/s41746-024-01102-y 38671232 PMC11053143 · DOI · Paper page
  51. Lee G. Jeong J. Seo S. Kim C. Kang P. (2018). Sentiment classification with word localization based on weakly supervised learning with a convolutional neural network. Knowledge-Based Syst. 152 (15), 70– 82. 10.1016/j.knosys.2018.04.006 · DOI
  52. Lee H. Kim S. T. Ro Y. M. (2019). Generation of multimodal justification using visual word constraint model for explainable computer-aided diagnosis, abs/1906.03922. CoRR, 21– 29. 10.1007/978-3-030-33850-3_3 · DOI
  53. Li A. Xiao F. Zhang C. Fan C. (2021). Attention-based interpretable neural network for building cooling load prediction. Appl. Energy 299, 117238. 10.1016/j.apenergy.2021.117238 · DOI
  54. Linardatos P. Papastefanopoulos V. Kotsiantis S. (2021) Entropy [preprint]. PMC7824368 33375658
  55. Lindsey R. Daluiski A. Chopra S. Lachapelle A. Mozer M. Sicular S. (2018). Deep neural network improves fracture detection by clinicians. Proc. Natl. Acad. Sci. U. S. A. 115 (45), 11591– 11596. 10.1073/pnas.1806905115 30348771 PMC6233134 · DOI
  56. Liu B. Guo J. Chen C. L. P. Wu X. Zhang T. (2023). Fine-grained interpretability for EEG emotion recognition: concat-aided grad-CAM and systematic brain functional network. IEEE Trans. Affect. Comput. 15, 671– 684. 10.1109/taffc.2023.3288885 · DOI
  57. Lo C.-M. Hung P.-H. Lin D.-T. (2021). Rapid assessment of acute ischemic stroke by computed tomography using deep convolutional neural networks. J. Digit. Imaging 34 (3), 637– 646. 10.1007/s10278-021-00457-y 33963421 PMC8329118 · DOI
  58. Lombardi A. Monaco A. Donvito G. Amoroso N. Bellotti R. Tangaro S. (2021). Brain age prediction with morphological features using deep neural networks: Results from predictive analytic competition 2019. Front. Psychiatry. 11, 619629. 33551880 PMC7854554
  59. Longo L. Brcic M. Cabitza F. Choi J. Confalonieri R. Ser J. D. (2024). Explainable Artificial Intelligence (XAI) 2.0: a manifesto of open challenges and interdisciplinary research directions. Inf. Fusion 106, 102301. 10.1016/j.inffus.2024.102301 · DOI
  60. Masood A. Sheng B. Li P. Hou X. Wei X. Qin J. (2018). Computer-assisted decision support system in pulmonary cancer detection and stage classification on CT images. J. Biomed. Inf. 79, 117– 128. 10.1016/j.jbi.2018.01.005 29366586 · DOI · Paper page
  61. McGenity C. Clarke E. L. Jennings C. Matthews G. Cartlidge C. Freduah-Agyemang H. (2024). Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. npj Digit. Med. 7 (1), 114. 10.1038/s41746-024-01106-8 38704465 PMC11069583 · DOI · Paper page
  62. Mella M. H. Chabrillac E. Dupret-Bories A. Mirallie M. Vergez S. (2023). Transoral robotic surgery for head and neck cancer: advances and residual knowledge gaps. J. Clin. Med. 12 (6), 2303. 10.3390/jcm12062303 36983308 PMC10056198 · DOI
  63. Mitchell R. Cooper J. Frank E. Holmes G. (2022). Sampling permutations for shapley value estimation. J. Mach. Learn. Res. 23 (43), 1– 46.
  64. Mohamed E. A. Rashed E. A. Gaber T. Karam O. (2022). Deep learning model for fully automated breast cancer detection system from thermograms. PLOS ONE 17 (1), 1– 20. 10.1371/journal.pone.0262349 PMC8759675 35030211 · DOI
  65. Moradi M. Samwald M. (2021). Post-hoc explanation of black-box classifiers using confident itemsets. Expert Syst. Appl. 165, 113941. 10.1016/j.eswa.2020.113941 · DOI
  66. Nicolae A. Semple M. Lu L. Smith M. Chung H. Loblaw A. (2020). Conventional vs machine learning-based treatment planning in prostate brachytherapy: results of a Phase I randomized controlled trial. Brachytherapy 19 (4), 470– 476. 10.1016/j.brachy.2020.03.004 32317241 · DOI
  67. Nielsen I. E. Dera D. Rasool G. Ramachandran R. P. Bouaynaya N. C. (2022). Robust explainability: a tutorial on gradient-based attribution methods for deep neural networks. IEEE Signal Process. Mag. 39 (4), 73– 84. 10.1109/msp.2022.3142719 · DOI
  68. Noh HS Park J. Sim G. Yu J. E. Chung Y. (2014). Nonparametric bayesian statistical models in biomedical research. Korean J. Appl. Statistics 27 (6), 867– 889. 10.5351/kjas.2014.27.6.867 · DOI
  69. Pang S. Ding T. Qiao S. Meng F. Wang S. Li P. (2019). A novel YOLOv3-arch model for identifying cholelithiasis and classifying gallstones on CT images. PLoS one 14 (6), e0217647. 10.1371/journal.pone.0217647 31211791 PMC6581241 · DOI
  70. Pavlidis G. (2024). Unlocking the black box: analysing the EU artificial intelligence act’s framework for explainability in AI. Law, Innovation Technol. 16, 293– 308. 10.1080/17579961.2024.2313795 · DOI
  71. Quiroz J. C. Feng Y. Z. Cheng Z. Y. Rezazadegan D. Chen P. K. Lin Q. T. (2021). Development and validation of a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data: retrospective study. JMIR Med. Inf. 9 (2), e24572. 10.2196/24572 PMC7879715 33534723 · DOI
  72. Rawson T. M. Hernandez B. Moore L. S. P. Herrero P. Charani E. Ming D. (2021). A real-world evaluation of a case-based reasoning algorithm to support antimicrobial prescribing decisions in acute care. Clin. Infect. Dis. 72 (12), 2103– 2111. 10.1093/cid/ciaa383 32246143 · DOI
  73. Reddy K. Gharde P. Tayade H. Patil M. Reddy L. S. Surya D. (2023). Advancements in robotic surgery: a comprehensive overview of current utilizations and upcoming frontiers. Cureus. 15 (12), e50415. 10.7759/cureus.50415 38222213 PMC10784205 · DOI
  74. Saeed W. Omlin C. (2023). Explainable AI (XAI): a systematic meta-survey of current challenges and future opportunities. Knowledge-Based Syst. 263, 110273. 10.1016/j.knosys.2023.110273 · DOI · Paper page
  75. Salem N. M. Jack K. M. Gu H. Kumar A. Garcia M. Yang P. (2023). Machine and deep learning identified metabolites and clinical features associated with gallstone disease. Comput. Methods Programs Biomed. Update 3, 100106. 10.1016/j.cmpbup.2023.100106 · DOI
  76. Savindu Herath Pathirannehelage Y. R. S. Krogh G. von von Krogh G. (2024). Design principles for artificial intelligence-augmented decision making: an action design research study. Eur. J. Inf. Syst. 0 (0), 1– 23. 10.1080/0960085X.2024.2330402 · DOI
  77. Schöttl A. (2020). “ A light-weight method to foster the (Grad) CAM interpretability and explainability of classification networks,” in 2020 10th international conference on advanced computer information technologies (ACIT), Deggendorf, Germany, September 16–18, 2020 (IEEE), 348– 351.
  78. Selvaraju R. R. (2017). “ Grad-cam: visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision, Venice, Italy, October 22–29, 2017 (IEEE), 618– 626.
  79. Serednicki W. A. Hołówko W. Major P. Małczak P. Pędziwiatr M. (2023). Minimizing blood loss and transfusion rate in laparoscopic liver surgery: a review. Wideochir. Inne. Tech. Maloinwazyjne 18 (2), 213– 223. 10.5114/wiitm.2022.124088 37680740 PMC10481434 · DOI
  80. Shapley L. S. (2016) “ 17. A value for n-person games,” in Contributions to the theory of games (AM-28), eds Kuhn H. W. Tucker A. W. Vol. II. (Princeton University Press), pp. 307– 318. 10.1515/9781400881970-018 · DOI · Paper page
  81. Shorfuzzaman M. Masud M. Alhumyani H. Anand D. Singh A. (2021). Artificial neural network-based deep learning model for COVID-19 patient detection using X-ray chest images. J. Healthc. Eng. 2021, 1– 16. 10.1155/2021/5513679 PMC8184332 34194681 · DOI
  82. Singh A. Sengupta S. Lakshminarayanan V. (2020). Explainable deep learning models in medical image analysis. arXiv 6, 52. 10.3390/jimaging6060052 PMC8321083 34460598 · DOI · Paper page
  83. Sinha S. Vegesna R. Mukherjee S. Kammula A. V. Dhruba S. R. Wu W. (2024). PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors. Nat. Cancer 5, 938– 952. 10.1038/s43018-024-00756-7 38637658 PMC13447014 · DOI
  84. Siouras A. Moustakidis S. Giannakidis A. Chalatsis G. Liampas I. Vlychou M. (2022). Knee injury detection using deep learning on MRI studies: a systematic review. Diagn. (Basel, Switzerland) 12 (2), 537. 10.3390/diagnostics12020537 PMC8871256 35204625 · DOI
  85. Stano M. Benesova W. Martak L. S. (2020). “ Explainable 3D convolutional neural network using GMM encoding,” in Twelfth international conference on machine vision (ICMV 2019). Editors Osten W. Nikolaev D. P. (SPIE), 507– 513. 10.1117/12.2557314 · DOI
  86. Stefanelli E. Cordella F. Gentile C. Zollo L. (2023). Hand prosthesis sensorimotor control inspired by the human somatosensory system. Robotics 12 (5), 136. 10.3390/robotics12050136 · DOI
  87. Stinson C. Vlaad S. (2024). A feeling for the algorithm: diversity, expertise, and artificial intelligence. Big Data & Soc. 11. 10.1177/20539517231224247 · DOI
  88. Strumbelj E. Kononenko I. (2011) ‘ A general method for visualizing and explaining black-box regression models,, 21– 30. 10.1007/978-3-642-20267-4_3 · DOI
  89. Subramani S. Varshney N. Anand M. V. Soudagar M. E. M. Al-Keridis L. A. Upadhyay T. K. (2023). Cardiovascular diseases prediction by machine learning incorporation with deep learning. Front. Med. 10, 1150933. 10.3389/fmed.2023.1150933 PMC10150633 37138750 · DOI
  90. Vinogradova K. Dibrov A. Myers G. (2020). “ Towards interpretable semantic segmentation via gradient-weighted class activation mapping (student abstract),” in Proceedings of the AAAI conference on artificial intelligence, New York, United States, February 2020, 13943– 13944.
  91. Wang P. Liu X. Berzin T. M. Glissen Brown J. R. Liu P. Zhou C. (2020). Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol. Hepatol 5 (4), 343– 351. 10.1016/S2468-1253(19)30411-X 31981517 · DOI · Paper page
  92. Wu X. Tao Y. Qiu Q. Wu X. (2018). Application of image recognition-based automatic hyphae detection in fungal keratitis. Australas. Phys. Eng. Sci. Med. 41 (1), 95– 103. 10.1007/s13246-017-0613-8 29285615 · DOI
  93. Wu Yazhou Y. D. Chen X. (2022). Research progress and prospect of artificial intelligence in the clinical field. J. Army Med. Univ. 44, 89– 102. 10.16016/j.1000-5404.202112092 · DOI
  94. Xie X. Li K. Wu Y. Ai C. Wang Q. Wang C. (2021). Preliminary experiences with robot-assisted choledochal cyst excision using the da vinci surgical system in children below the age of one. Front. Pediatr. 23 (9), 741098. 10.3389/fped.2021.741098 PMC8496349 34631631 · DOI
  95. Yang W. Wei Y. Wei H. Chen Y. Huang G. Li X. (2023). Survey on explainable AI: from approaches, limitations and applications aspects. Human-Centric Intell. Syst. 3 (3), 161– 188. 10.1007/s44230-023-00038-y · DOI
  96. Yoo Y. J. Ha E. J. Cho Y. J. Kim H. L. Han M. Kang S. Y. (2018). Computer-aided diagnosis of thyroid nodules via ultrasonography: initial clinical experience. Korean J. Radiol. 19 (4), 665– 672. 10.3348/kjr.2018.19.4.665 29962872 PMC6005935 · DOI
  97. Zadeh Shirazi A. McDonnell M. D. Fornaciari E. Bagherian N. S. Scheer K. G. Samuel M. S. (2021). A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma. Br. J. Cancer 125 (3), 337– 350. 10.1038/s41416-021-01394-x 33927352 PMC8329064 · DOI
  98. Zheng S. Rammohan N. Sita T. Teo P. T. Wu Y. Lesniak M. (2024). GlioPredictor: a deep learning model for identification of high-risk adult IDH-mutant glioma towards adjuvant treatment planning. Sci. Rep. 14 (1), 2126. 10.1038/s41598-024-51765-6 38267516 PMC10808248 · DOI

Cited by

Sorted by citations

Authors
Ennab, M., & Mcheick, H.
Affiliations
Université du Québec à Chicoutimi (CA)
Corresponding author
Ennab, Mohammad
ORCID
Venue
Frontiers in Robotics and AI
Publisher
Frontiers Media SA
Published
2024-11-28
Volume
11
Pages
1444763-1444763
ISSN
2296-9144 (ISSN-L)
DOI
10.3389/frobt.2024.1444763
PMC
PMC11638409
OpenAlex
W4404817209
Cited by
238
References
8
Citations by year
2026: 154 · 2025: 82 · 2024: 1 · 2023: 1
FWCI
8.15
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Robotics and AI (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
explainable AI, model accuracy, artificial intelligence in healthcare, black-box models, deep learning, patient safety, predictive modeling, generalizability across populations, clinical decision support, transparency

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