Revolutionizing Healthcare with AI: Alleviating Disparities, Reporting Outcomes, and Tailoring to Local Needs

– Special issue of International Journal of Pattern Recognition and Artificial Intelligence
– Expands upon papers from FLAIRS conference, covers various AI techniques and applications

10.1038/s43856-023-00279-9

In this article , the authors proposed that AI should be designed to alleviate health disparities, report clinically meaningful outcomes, reduce overdiagnosis and overtreatment, have high healthcare value, consider biographical drivers of health, be easily tailored to the local population, promote a learning healthcare system, and facilitate shared decision-making.

– AI and computational technology are revolutionizing interior design graphics and modeling.
– These technologies offer benefits such as design iterations, material visualization, and time optimization.

– AI-based tools are transforming oncology clinical applications for personalized care.
– Challenges in applying AI-based tools in cancer care are discussed.

– AI should be designed to alleviate health disparities, report clinically meaningful outcomes, reduce overdiagnosis and overtreatment, have high healthcare value, consider biographical drivers of health, be easily tailored to the local population, promote a learning healthcare system, and facilitate shared decision-making.
– The principles are illustrated by examples from breast cancer research.

The paper discusses the need for AI tools in healthcare to address high-priority healthcare needs and have high healthcare value. It also suggests that AI tools should be easily tailored to the local population and reduce overdiagnosis compared to existing approaches.

– Clear and comprehensive guidance is needed for AI developers.
– The proposed principles will raise the standard for AI tools in healthcare.

– Digital technology and tools are being used in the design industry.
– Artificial Intelligence (AI) is one of the latest computational technologies being utilized.

– The paper reviews existing AI technology and its potential applications in manufacturing systems.
– The paper discusses tools and techniques of AI relevant to the manufacturing environment.

– The paper discusses the application of stored programs in Artificial Intelligence.
– It focuses on production systems and their role in rule-based expert systems.