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Title: :  Healthcare Explainable Artificial Intelligence- Possibilities, Challenges, and a Fresh Perspective on the Problem Space
PaperId: :  21799
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 5 2023
DUI:    16.0415/IJARIIE-21799
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Sathishkumar MVels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai
Dr. Raghavendran VVels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai

Abstract

computer Engineering
Artificial Intelligence, XAI, Explainable AI, Interpretability, Machine Learning, and AI in Healthcare
Despite the potential that XAI can offer to the application of AI in this business, explainable artificial intelligence (XAI) is still in the early stages of acceptance in the healthcare sector. Standards for explanations, the level of interaction between various stakeholders and the models, the implementation of quality and performance metrics, the agreement on standards for safety and accountability, its integration into clinical workflows, and IT infrastructure are just a few of the issues that still need to be resolved. There are two goals for this Paper. The first one involves summarizing the findings of a literature review and highlighting the current state of explain ability, including any gaps, difficulties, and opportunities for XAI in the healthcare sector. We advise using a combined taxonomy to group explain-ability methodologies in order to facilitate understanding and onboarding into this field of study. The second goal is to determine whether using a novel strategy to analyze the explainability problem space through a particular problem or domain lens and automating that method in an AutoML-like manner would help reduce the issues outlined above. The literature has a propensity to view the explainability of AI through a model-first lens, which ignores real-world issues and domains. For instance, the explainability of a patient's survival model is handled similarly to the calculation of a hospital's procedure cost. We can (semi-)automatically find appropriate models, optimize their parameters and their explanations, metrics, stakeholders, safety/accountability level, and suggest ways of integrating them into clinical workflow when the problem or domain to which XAI should be applied is clearly defined.

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IJARIIE Sathishkumar M, and Dr. Raghavendran V. "Healthcare Explainable Artificial Intelligence- Possibilities, Challenges, and a Fresh Perspective on the Problem Space" International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 5 2023 Page 1988-1991
MLA Sathishkumar M, and Dr. Raghavendran V. "Healthcare Explainable Artificial Intelligence- Possibilities, Challenges, and a Fresh Perspective on the Problem Space." International Journal Of Advance Research And Innovative Ideas In Education 9.5(2023) : 1988-1991.
APA Sathishkumar M, & Dr. Raghavendran V. (2023). Healthcare Explainable Artificial Intelligence- Possibilities, Challenges, and a Fresh Perspective on the Problem Space. International Journal Of Advance Research And Innovative Ideas In Education, 9(5), 1988-1991.
Chicago Sathishkumar M, and Dr. Raghavendran V. "Healthcare Explainable Artificial Intelligence- Possibilities, Challenges, and a Fresh Perspective on the Problem Space." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 5 (2023) : 1988-1991.
Oxford Sathishkumar M, and Dr. Raghavendran V. 'Healthcare Explainable Artificial Intelligence- Possibilities, Challenges, and a Fresh Perspective on the Problem Space', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 5, 2023, p. 1988-1991. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Healthcare_Explainable_Artificial_Intelligence__Possibilities__Challenges__and_a_Fresh_Perspective_on_the_Problem_Space_ijariie21799.pdf (Accessed : ).
Harvard Sathishkumar M, and Dr. Raghavendran V. (2023) 'Healthcare Explainable Artificial Intelligence- Possibilities, Challenges, and a Fresh Perspective on the Problem Space', International Journal Of Advance Research And Innovative Ideas In Education, 9(5), pp. 1988-1991IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Healthcare_Explainable_Artificial_Intelligence__Possibilities__Challenges__and_a_Fresh_Perspective_on_the_Problem_Space_ijariie21799.pdf (Accessed : )
IEEE Sathishkumar M, and Dr. Raghavendran V, "Healthcare Explainable Artificial Intelligence- Possibilities, Challenges, and a Fresh Perspective on the Problem Space," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 5, pp. 1988-1991, Sep-Oct 2023. [Online]. Available: http://ijariie.com/AdminUploadPdf/Healthcare_Explainable_Artificial_Intelligence__Possibilities__Challenges__and_a_Fresh_Perspective_on_the_Problem_Space_ijariie21799.pdf [Accessed : ].
Turabian Sathishkumar M, and Dr. Raghavendran V. "Healthcare Explainable Artificial Intelligence- Possibilities, Challenges, and a Fresh Perspective on the Problem Space." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 5 ().
Vancouver Sathishkumar M, and Dr. Raghavendran V. Healthcare Explainable Artificial Intelligence- Possibilities, Challenges, and a Fresh Perspective on the Problem Space. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : ]; 9(5) : 1988-1991. Available from: http://ijariie.com/AdminUploadPdf/Healthcare_Explainable_Artificial_Intelligence__Possibilities__Challenges__and_a_Fresh_Perspective_on_the_Problem_Space_ijariie21799.pdf
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