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Journal code(s)
- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
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Vol. 8, Special Issue 2 (2019)
Explainable AI in healthcare: A theoretical overview of interpretable models for medical diagnosis
Author(s):
Roovi Goswami, Sangeeta Yadav and Vinod Kumar
Roovi Goswami, Sangeeta Yadav and Vinod Kumar
Abstract:
In recent years, the integration of Artificial Intelligence (AI) in healthcare has shown remarkable potential for enhancing medical diagnosis and treatment. However, the opacity of complex AI models poses a significant challenge in gaining trust and acceptance from healthcare practitioners and patients. The demand for transparency and interpretability in AI systems, particularly in the context of medical diagnosis, has led to the emergence of Explainable AI (XAI) as a critical area of research. This theoretical overview aims to explore and elucidate the role of interpretable models in healthcare, focusing on their applications and implications for medical diagnosis.
In recent years, the integration of Artificial Intelligence (AI) in healthcare has shown remarkable potential for enhancing medical diagnosis and treatment. However, the opacity of complex AI models poses a significant challenge in gaining trust and acceptance from healthcare practitioners and patients. The demand for transparency and interpretability in AI systems, particularly in the context of medical diagnosis, has led to the emergence of Explainable AI (XAI) as a critical area of research. This theoretical overview aims to explore and elucidate the role of interpretable models in healthcare, focusing on their applications and implications for medical diagnosis.
The first section of this review paper provides a comprehensive examination of the current landscape of AI in healthcare and the pivotal role it plays in medical diagnosis. With the increasing complexity of AI algorithms, the lack of interpretability becomes a bottleneck, hindering the widespread adoption of these technologies in clinical settings. In response to this challenge, interpretable models have gained prominence as they offer insights into the decision-making processes of AI systems.
Subsequently, the paper delves into various interpretable models employed in medical diagnosis, including rule-based systems, decision trees, and linear models. Each model is scrutinized for its strengths and limitations, highlighting the trade-offs between interpretability and predictive performance. The discussion extends to the importance of incorporating domain knowledge and expert input in the development of these models to ensure their clinical relevance and applicability.
Furthermore, the review addresses the ethical considerations associated with Explainable AI in healthcare, emphasizing the need for transparent and accountable AI systems. Balancing the interpretability of models with the protection of sensitive patient information is crucial to maintaining trust and adherence to privacy standards.
Pages: 29-33 | 256 Views 153 Downloads
How to cite this article:
Roovi Goswami, Sangeeta Yadav and Vinod Kumar. Explainable AI in healthcare: A theoretical overview of interpretable models for medical diagnosis. The Pharma Innovation Journal. 2019; 8(2S): 29-33. DOI: 10.22271/tpi.2019.v8.i2Sa.25246
Journal code(s)
- E-ISSN Number: 2277-7695
- P-ISSN Number: 2349-8242
- CODEN Code: PIHNBQ
- ZDB-Number: 2663038-2
- IC Journal ID: 7725
Main Menu
Special Issue
Copyright Form
Identifier
The Pharma Innovation Journal
