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The objective of an explainable AI (XAI) system is to enhance the transparency in its actions by providing clear justifications. To design AI systems that are both efficient and comprehensible to humans, certain guidelines should be followed: the XAIExplainable Artificial Intelligence (XAI) system must elucidate its capabilities and understandings, detail its past actions, describe its current operations, and predict future actions. Additionally, it should disclose the critical information driving its decisions. As AI-powered systems advance, they often require minimal human intervention for development and deployment. In the realm of geneticBiomaterial analysis biomaterial analysisGenetic biomaterial analysis, XAIExplainable Artificial Intelligence (XAI) is crucial for interpreting complex genetic data, understanding the interactions between different genetic markers, and predicting the outcomes of genetic modifications. For instance, XAIExplainable Artificial Intelligence (XAI) can help researchers comprehend the underlying genetic pathways involved in disease mechanisms and the effects of various genetic treatments. This transparency is essential for ensuring the reliability and validity of genetic studies, which can ultimately lead to better-targeted therapies and personalized medicine. In many developing countries, AI is already widely used to diagnose COVID-19 using medical imaging techniques like CT scans and chest X-rays. The XAIExplainable Artificial Intelligence (XAI) framework proposes the development of multiple machine learning models in this medical context, aiming to produce more interpretable processes while maintaining high diagnostic accuracy. These models also offer a tool for doctors to analyse, comprehend, and make informed decisions. In this chapter, we explore the significance of XAIExplainable Artificial Intelligence (XAI) in the analysis of genetic biomaterials and examine its potential roles in various applications, including diagnosis, analysis, surgical practices, and disease predictions. We highlight how XAIExplainable Artificial Intelligence (XAI) can increase the trustworthiness and transparency of AI models in genetic research, enabling clinicians and scientists to make better-informed decisions based on the AI's findings.

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XAI Approaches in Genetic Biomaterial Analysis

  • 章节

  • 发表日期：

    2025年

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  • Taniya Rose Abraham,
  • Davis Johny,
  • N. Sajid Babu,
  • Mary Varughese,
  • Ajmal Parveena

摘要

The objective of an explainable AI (XAI) system is to enhance the transparency in its actions by providing clear justifications. To design AI systems that are both efficient and comprehensible to humans, certain guidelines should be followed: the XAIExplainable Artificial Intelligence (XAI) system must elucidate its capabilities and understandings, detail its past actions, describe its current operations, and predict future actions. Additionally, it should disclose the critical information driving its decisions. As AI-powered systems advance, they often require minimal human intervention for development and deployment. In the realm of geneticBiomaterial analysis biomaterial analysisGenetic biomaterial analysis, XAIExplainable Artificial Intelligence (XAI) is crucial for interpreting complex genetic data, understanding the interactions between different genetic markers, and predicting the outcomes of genetic modifications. For instance, XAIExplainable Artificial Intelligence (XAI) can help researchers comprehend the underlying genetic pathways involved in disease mechanisms and the effects of various genetic treatments. This transparency is essential for ensuring the reliability and validity of genetic studies, which can ultimately lead to better-targeted therapies and personalized medicine. In many developing countries, AI is already widely used to diagnose COVID-19 using medical imaging techniques like CT scans and chest X-rays. The XAIExplainable Artificial Intelligence (XAI) framework proposes the development of multiple machine learning models in this medical context, aiming to produce more interpretable processes while maintaining high diagnostic accuracy. These models also offer a tool for doctors to analyse, comprehend, and make informed decisions. In this chapter, we explore the significance of XAIExplainable Artificial Intelligence (XAI) in the analysis of genetic biomaterials and examine its potential roles in various applications, including diagnosis, analysis, surgical practices, and disease predictions. We highlight how XAIExplainable Artificial Intelligence (XAI) can increase the trustworthiness and transparency of AI models in genetic research, enabling clinicians and scientists to make better-informed decisions based on the AI's findings.