Authors :
Sai Mani Krishna Sistla; Vathsala Periyasamy; Jawaharbabu Jeyaraman
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
https://tinyurl.com/35vps3ym
Scribd :
https://tinyurl.com/2wckw9js
DOI :
https://doi.org/10.5281/zenodo.10776780
Abstract :
Explainable AI techniques are increasingly
crucial in healthcare, where transparency and
interpretability of artificial intelligence (AI) models are
paramount. In domains like medical imaging and clinical
decision-making, AI serves to elucidate the rationale
behind AI-driven decisions, emulating human reasoning to
bolster trust and acceptance. However, the implementation
of XAI in healthcare is not without challenges, including
algorithmic bias, operational speed, and the necessity for
multidisciplinary collaboration to navigate technical, legal,
medical, and patient-centric considerations effectively. By
providing explanations to healthcare professionals, AI
fosters trust and ensures the judicious use of AI tools.
Overcoming issues such as bias in algorithmic outputs
derived from data and interactions is essential for
maintaining fairness in personalized medicine
applications. Various XAI techniques, such as causal
explanations and interactive interfaces, facilitate improved
human-computer interactions by making AI decisions
comprehensible and reliable. The development and
deployment of XAI in clinical settings offer transparency
to AI models but require concerted efforts to address
practical concerns like speed, bias mitigation, and
interdisciplinary cooperation to uphold the ethical and
efficient utilization of AI in healthcare. Through the
strategic application of XAI techniques, healthcare
practitioners can leverage transparent and trustworthy AI
systems to enhance decision-making processes and patient
outcomes.
Keywords :
Explainable AI (XAI), Healthcare, Radiomics, Human Judgment, Medical Imaging, Deep Learnings.
Explainable AI techniques are increasingly
crucial in healthcare, where transparency and
interpretability of artificial intelligence (AI) models are
paramount. In domains like medical imaging and clinical
decision-making, AI serves to elucidate the rationale
behind AI-driven decisions, emulating human reasoning to
bolster trust and acceptance. However, the implementation
of XAI in healthcare is not without challenges, including
algorithmic bias, operational speed, and the necessity for
multidisciplinary collaboration to navigate technical, legal,
medical, and patient-centric considerations effectively. By
providing explanations to healthcare professionals, AI
fosters trust and ensures the judicious use of AI tools.
Overcoming issues such as bias in algorithmic outputs
derived from data and interactions is essential for
maintaining fairness in personalized medicine
applications. Various XAI techniques, such as causal
explanations and interactive interfaces, facilitate improved
human-computer interactions by making AI decisions
comprehensible and reliable. The development and
deployment of XAI in clinical settings offer transparency
to AI models but require concerted efforts to address
practical concerns like speed, bias mitigation, and
interdisciplinary cooperation to uphold the ethical and
efficient utilization of AI in healthcare. Through the
strategic application of XAI techniques, healthcare
practitioners can leverage transparent and trustworthy AI
systems to enhance decision-making processes and patient
outcomes.
Keywords :
Explainable AI (XAI), Healthcare, Radiomics, Human Judgment, Medical Imaging, Deep Learnings.
