Challenges of developing artificial intelligence-assisted tools for clinical medicine.2021

Machine learning, a subset of artificial intelligence (AI), is a set of computational tools that can be used to enhance provision of clinical care in all areas of medicine. Gastroenterology and hepatology utilize multiple sources of information, including visual findings on endoscopy, radiologic imaging, manometric testing, genomes, proteomes, and metabolomes. However, clinical care is complex and requires a thoughtful approach to best deploy AI tools to improve quality of care and bring value to patients and providers. On the operational level, AI-assisted clinical management should consider logistic challenges in care delivery, data management, and algorithmic stewardship. There is still much work to be done on a broader societal level in developing ethical, regulatory, and reimbursement frameworks. A multidisciplinary approach and awareness of AI tools will create a vibrant ecosystem for using AI-assisted tools to guide and enhance clinical practice. From optically enhanced endoscopy to clinical decision support for risk stratification, AI tools will potentially transform our practice by leveraging massive amounts of data to personalize care to the right patient, in the right amount, at the right time.

– Machine learning can enhance clinical care in medicine.
– Challenges in deploying AI tools for clinical practice.

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10.1111/JGH.15378

In this paper, a multidisciplinary approach and awareness of AI tools will create a vibrant ecosystem for using AI-assisted tools to guide and enhance clinical practice by leveraging massive amounts of data to personalize care to the right patient, in the right amount, at the right time.

– Machine learning can enhance clinical care in all areas of medicine.
– AI tools have the potential to personalize care based on data.

– 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 tools can enhance clinical care in medicine.
– Ethical, regulatory, and reimbursement frameworks need development.

– The paper explores healthcare staff perceptions on the benefits and challenges of using AI predictive tools in clinical decision-making.
– The study identifies opportunities for the application of AI predictive tools in clinical practice.

The paper discusses the challenges of developing AI-assisted tools for clinical medicine, including logistic challenges in care delivery, data management, and algorithmic stewardship. It mentions that AI tools can be used to enhance clinical care in all areas of medicine.

– Machine learning
– AI-assisted clinical management

– Logistic challenges in care delivery, data management, and algorithmic stewardship.
– Need for ethical, regulatory, and reimbursement frameworks.

– 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.

– AI tools can enhance clinical care in medicine.
– Challenges include logistic, ethical, regulatory, and reimbursement frameworks.