Keywords
Interpretability
Trust
Medical Diagnosis
Healthcare
Machine Learning
Transparency
XAI Techniques
Patient Engagement
Regulatory Compliance
How to Cite
Abstract
Explainable Artificial Intelligence (XAI) has emerged as a critical area of research to enhance the transparency and interpretability of complex machine learning models, particularly in the context of medical diagnosis. This paper explores the implementation of XAI techniques to provide transparent explanations for medical diagnoses, aiming to improve trust and acceptance among healthcare professionals and patients. The paper begins by discussing the importance of interpretability in healthcare AI, highlighting the challenges posed by black-box models. It then presents a comprehensive review of XAI techniques applicable to medical diagnosis, including rule-based approaches, model-agnostic methods, and post-hoc explanation techniques. The paper also discusses the implications of XAI for healthcare, including improved decision-making, patient engagement, and regulatory compliance. Finally, the paper concludes with a discussion on future research directions and the potential impact of XAI on the field of medical diagnosis.
References
Tatineni, Sumanth. "Applying DevOps Practices for Quality and Reliability Improvement in Cloud-Based Systems." Technix international journal for engineering research (TIJER)10.11 (2023): 374-380.
Doshi-Velez, Finale, and Been Kim. "Towards A Rigorous Science of Interpretable Machine Learning." arXiv preprint arXiv:1702.08608 (2017).
Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. "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.
Guidotti, Riccardo, et al. "A survey of methods for explaining black box models." ACM Computing Surveys (CSUR) 51.5 (2018): 1-42.
Caruana, Rich, et al. "Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission." Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2015.
Holzinger, Andreas, et al. "Biomedical informatics: explaining machine learning in medical informatics." Bioinformatics 24.5 (2008): 623-628.
Poursaberi, Ahmad, et al. "Toward interpretable deep neural networks for EEG-based diagnosis of neurological disorders." Brain Informatics 6.1 (2019): 4.
Zhang, Li, et al. "Interpretable convolutional neural networks for effective seismic interpretation." Interpretation 7.3 (2019): T877-T889.
Chen, Xi, et al. "Interpretable deep learning for seismic imaging: Image reconstruction from sparse data." GEOPHYSICS 84.2 (2019): R165-R179.
Yang, Hui, et al. "A deep learning model integrating FCNNs and CRFs for brain tumor segmentation." Medical image analysis 36 (2017): 18-27.
Selvaraju, Ramprasaath R., et al. "Grad-CAM: Visual explanations from deep networks via gradient-based localization." Proceedings of the IEEE international conference on computer vision. 2017.
Choi, Edward, et al. "Doctor AI: Predicting clinical events via recurrent neural networks." Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016.
Murdoch, W. James, et al. "Definitions, methods, and applications in interpretable machine learning." Proceedings of the National Academy of Sciences 116.44 (2019): 22071-22080.
Langer, Kirsten, et al. "Explainable artificial intelligence and machine learning: a reality rooted perspective." Neurocomputing 376 (2020): 218-227.
Mittelstadt, Brent Daniel, Chris Russell, and Sandra Wachter. "Explaining explanations in AI." Proceedings of the conference on fairness, accountability, and transparency. 2019.
Adebayo, Julius, et al. "Sanity checks for saliency maps." Advances in Neural Information Processing Systems. 2018.
Maruthi, Srihari, et al. "Deconstructing the Semantics of Human-Centric AI: A Linguistic Analysis." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 11-30.
Dodda, Sarath Babu, et al. "Ethical Deliberations in the Nexus of Artificial Intelligence and Moral Philosophy." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 31-43.
Zanke, Pankaj. "AI-Driven Fraud Detection Systems: A Comparative Study across Banking, Insurance, and Healthcare." Advances in Deep Learning Techniques 3.2 (2023): 1-22.
Biswas, A., and W. Talukdar. “Robustness of Structured Data Extraction from In-Plane Rotated Documents Using Multi-Modal Large Language Models (LLM)”. Journal of Artificial Intelligence Research, vol. 4, no. 1, Mar. 2024, pp. 176-95, https://thesciencebrigade.com/JAIR/article/view/219.
Maruthi, Srihari, et al. "Toward a Hermeneutics of Explainability: Unraveling the Inner Workings of AI Systems." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 27-44.
Biswas, Anjanava, and Wrick Talukdar. "Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation." arXiv preprint arXiv:2405.18346 (2024).
Yellu, Ramswaroop Reddy, et al. "AI Ethics-Challenges and Considerations: Examining ethical challenges and considerations in the development and deployment of artificial intelligence systems." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 9-16.
Maruthi, Srihari, et al. "Automated Planning and Scheduling in AI: Studying automated planning and scheduling techniques for efficient decision-making in artificial intelligence." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 14-25.
Ambati, Loknath Sai, et al. "Impact of healthcare information technology (HIT) on chronic disease conditions." MWAIS Proc 2021 (2021).
Singh, Amarjeet, and Alok Aggarwal. "Securing Microservice CICD Pipelines in Cloud Deployments through Infrastructure as Code Implementation Approach and Best Practices." Journal of Science & Technology 3.3 (2022): 51-65.
Zanke, Pankaj. "Enhancing Claims Processing Efficiency Through Data Analytics in Property & Casualty Insurance." Journal of Science & Technology 2.3 (2021): 69-92.
Pulimamidi, R., and G. P. Buddha. "Applications of Artificial Intelligence Based Technologies in The Healthcare Industry." Tuijin Jishu/Journal of Propulsion Technology 44.3: 4513-4519.
Dodda, Sarath Babu, et al. "Conversational AI-Chatbot Architectures and Evaluation: Analyzing architectures and evaluation methods for conversational AI systems, including chatbots, virtual assistants, and dialogue systems." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 13-20.
Ponnusamy, Sivakumar, and Dinesh Eswararaj. "Modernization of Legacy Applications and Data: A Comprehensive Review on Implementation Challenges, Effective Strategies and Best Practices." (2024): 81-106.
Maruthi, Srihari, et al. "Language Model Interpretability-Explainable AI Methods: Exploring explainable AI methods for interpreting and explaining the decisions made by language models to enhance transparency and trustworthiness." Australian Journal of Machine Learning Research & Applications 2.2 (2022): 1-9.
Dodda, Sarath Babu, et al. "Federated Learning for Privacy-Preserving Collaborative AI: Exploring federated learning techniques for training AI models collaboratively while preserving data privacy." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 13-23.
Zanke, Pankaj. "Machine Learning Approaches for Credit Risk Assessment in Banking and Insurance." Internet of Things and Edge Computing Journal 3.1 (2023): 29-47.
Maruthi, Srihari, et al. "Temporal Reasoning in AI Systems: Studying temporal reasoning techniques and their applications in AI systems for modeling dynamic environments." Journal of AI-Assisted Scientific Discovery 2.2 (2022): 22-28.
Yellu, Ramswaroop Reddy, et al. "Transferable Adversarial Examples in AI: Examining transferable adversarial examples and their implications for the robustness of AI systems." Hong Kong Journal of AI and Medicine 2.2 (2022): 12-20.
Reddy Yellu, R., et al. "Transferable Adversarial Examples in AI: Examining transferable adversarial examples and their implications for the robustness of AI systems. Hong Kong Journal of AI and Medicine, 2 (2), 12-20." (2022).
Zanke, Pankaj, and Dipti Sontakke. "Artificial Intelligence Applications in Predictive Underwriting for Commercial Lines Insurance." Advances in Deep Learning Techniques 1.1 (2021): 23-38.
Singh, Amarjeet, and Alok Aggarwal. "Artificial Intelligence based Microservices Pod configuration Management Systems on AWS Kubernetes Service." Journal of Artificial Intelligence Research 3.1 (2023): 24-37.