Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

STEM Education Research: Useful Ideas for College Instructors Using Ballooning

Stratospheric ballooning is a tool of enormous promise to help STEM college faculty foster highly engaged learning of science for a wide range of students at a wide range of institutions. High altitude ballooning offers a platform for investigating science and engineering across many fields. Ballooning has been used in courses and experiences for not just undergraduate science majors, but also all undergraduates—including future teachers who, in turn, are the key to improving K-12 science education. Technological advances are lowering the cost of and expertise levels required to make launches and analyze data. Ballooning offers a context within which faculty can use effective research-based educational practices. Studies document that while many faculty are aware of STEM education research findings and new instructional practices, many fewer understand the underlying pedagogical principles behind the practices and have been able to implement new practices effectively. Changing one’s instructional practice is difficult to do and educators trying to do this need appropriate supports including time to try, reflect, try again, discuss with others, etc. Given this, how faculty implement new practices can be widely variable with different levels of success in engaging students successfully in learning STEM content. This session reviews a selection of STEM education research topics and focuses on concrete examples to help college instructors involved in ballooning. Specific facets of instructional practice are examined as are findings related to how to support faculty as they develop professionally and work to implement new practices

STEM Education Research: Useful Ideas for College Instructors Using Ballooning

Stratospheric ballooning is a tool of enormous promise to help STEM college faculty foster highly engaged learning of science for a wide range of students at a wide range of institutions. High altitude ballooning offers a platform for investigating science and engineering across many fields. Ballooning has been used in courses and experiences for not just undergraduate science majors, but also all undergraduates—including future teachers who, in turn, are the key to improving K-12 science education. Technological advances are lowering the cost of and expertise levels required to make launches and analyze data. Ballooning offers a context within which faculty can use effective research-based educational practices. Studies document that while many faculty are aware of STEM education research findings and new instructional practices, many fewer understand the underlying pedagogical principles behind the practices and have been able to implement new practices effectively. Changing one’s instructional practice is difficult to do and educators trying to do this need appropriate supports including time to try, reflect, try again, discuss with others, etc. Given this, how faculty implement new practices can be widely variable with different levels of success in engaging students successfully in learning STEM content. This session reviews a selection of STEM education research topics and focuses on concrete examples to help college instructors involved in ballooning. Specific facets of instructional practice are examined as are findings related to how to support faculty as they develop professionally and work to implement new practices

The Effect of Select Personal Care Ingredients and Simple Formulations on the Attachment of Bacteria on Polystyrene

The human body is covered with bacteria that are required for health and wellbeing. Additionally, there are pathogenic bacteria that are unwanted. It is therefore important to understand how personal care ingredients interact with these bacteria. To help understand these interactions, a high-throughput assay was developed to study the effect of personal care ingredients on attachment. Seventeen personal care ingredients were assayed singly and in simple alcohol based formulations. Three of the ingredients decreased the attachment of both bacteria tested by 90% singly and in formulation. Personal care ingredients singly and in simple formulations can prevent the attachment of bacteria. Further research is needed to better understand how personal care ingredients affect bacterial attachment and how these effects can be used to create new hygiene products for consumers