Artificial Intelligence in Healthcare Opportunities and Challenges for Personalized Medicine DOI Creative Commons

Kavyashree Nagarajaiah,

Gudla Sirisha,

Lokasani Bhanuprakash

и другие.

ITM Web of Conferences, Год журнала: 2025, Номер 76, С. 04006 - 04006

Опубликована: Янв. 1, 2025

The rise of artificial intelligence (AI) has revolutionized many sectors including healthcare, which benefitted from unique opportunities to harness AI-based personalized medicine. Despite the promise ML, there are certain challenges like data bias, a lack explainability, ethical concerns, high computational costs, and regulatory constraints that have limited its widespread usage in real world. This study outlines novel medicine framework for next generation AI systems overcomes these obstacles through utilization explainable (XAI), federated learning (FL) techniques additionally bolster privacy, adaptive models, optimization cost-efficient edge computing capabilities. provides foundation developing ethical, transparent, scalable approaches integrating into clinical workflows, as an assistive rather than replacement tool health care professionals. These advancements include implementing human-AI collaboration standardized evaluation metrics, augmenting domain-specific applications, collectively improve diagnostic precision, treatment efficacy, accessibility healthcare systems. Thus, proposed system will close translation gap between laboratory field, ultimately resulting is inclusive, efficient, global.

Язык: Английский

Artificial Intelligence in Healthcare Opportunities and Challenges for Personalized Medicine DOI Creative Commons

Kavyashree Nagarajaiah,

Gudla Sirisha,

Lokasani Bhanuprakash

и другие.

ITM Web of Conferences, Год журнала: 2025, Номер 76, С. 04006 - 04006

Опубликована: Янв. 1, 2025

The rise of artificial intelligence (AI) has revolutionized many sectors including healthcare, which benefitted from unique opportunities to harness AI-based personalized medicine. Despite the promise ML, there are certain challenges like data bias, a lack explainability, ethical concerns, high computational costs, and regulatory constraints that have limited its widespread usage in real world. This study outlines novel medicine framework for next generation AI systems overcomes these obstacles through utilization explainable (XAI), federated learning (FL) techniques additionally bolster privacy, adaptive models, optimization cost-efficient edge computing capabilities. provides foundation developing ethical, transparent, scalable approaches integrating into clinical workflows, as an assistive rather than replacement tool health care professionals. These advancements include implementing human-AI collaboration standardized evaluation metrics, augmenting domain-specific applications, collectively improve diagnostic precision, treatment efficacy, accessibility healthcare systems. Thus, proposed system will close translation gap between laboratory field, ultimately resulting is inclusive, efficient, global.

Язык: Английский

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