Editorial: Surfacing best practices for AI software development and integration in
healthcare

Sendak, Mark; Vidal, David; Trujillo, Sylvia; Singh, Karandeep; Liu, Xiaoxuan; Balu, Suresh

doi:10.3389/fdgth.2023.1150875

Cited by 4 publications

(6 citation statements)

References 20 publications

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“…The case studies served as real-world examples to facilitate ideation and discussion during the workshop among participants. More information about the workshop is presented in the accompanying Formal Comment [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

“…To address these gaps and tackle a common challenge faced by healthcare delivery organization leaders, we, the Health AI Partnership (HAIP), organized a case-based workshop [ 36 ] and developed a framework to assess how the use of AI solution might affect health equity. In the present research, we define health equity as the attainment of the optimal health for all people regardless of race , ethnicity , disability , sexual orientation , gender identity , socioeconomic status , geography , preferred language , and other factors that may affect access to care and health outcomes [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

Kim,

Hasan,

Kellogg

et al. 2024

PLOS Digit Health

Self Cite

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The use of data-driven technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is growing in healthcare. However, the proliferation of healthcare AI tools has outpaced regulatory frameworks, accountability measures, and governance standards to ensure safe, effective, and equitable use. To address these gaps and tackle a common challenge faced by healthcare delivery organizations, a case-based workshop was organized, and a framework was developed to evaluate the potential impact of implementing an AI solution on health equity. The Health Equity Across the AI Lifecycle (HEAAL) is co-designed with extensive engagement of clinical, operational, technical, and regulatory leaders across healthcare delivery organizations and ecosystem partners in the US. It assesses 5 equity assessment domains–accountability, fairness, fitness for purpose, reliability and validity, and transparency–across the span of eight key decision points in the AI adoption lifecycle. It is a process-oriented framework containing 37 step-by-step procedures for evaluating an existing AI solution and 34 procedures for evaluating a new AI solution in total. Within each procedure, it identifies relevant key stakeholders and data sources used to conduct the procedure. HEAAL guides how healthcare delivery organizations may mitigate the potential risk of AI solutions worsening health inequities. It also informs how much resources and support are required to assess the potential impact of AI solutions on health inequities.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

Kim,

Hasan,

Kellogg

et al. 2024

PLOS Digit Health

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

“…Seventy-seven people with various domains of expertise from 10 healthcare delivery organizations and 4 ecosystem partners attended the workshop. Clinical, technical, operational, and regulatory stakeholders as well as AI ethics experts shared their perspectives on the workshop topic through different activities as described in the accompanying Formal Comment [35]. Design researchers took notes of the discussions that took place during the workshop.…”

Section: Methodsmentioning

confidence: 99%

“…To address these gaps and tackle a common challenge faced by healthcare delivery organizations, we, the Health AI Partnership (HAIP), organized a case-based workshop [35] and developed a framework to assess the potential impact of a new AI solution on health equity. In the present research, we define health equity as the attainment of the optimal health for all people regardless of race, ethnicity, disability, sexual orientation, gender identity, socioeconomic status, geography, preferred language, and other factors that may affect access to care and health outcomes [36].…”

Section: Present Researchmentioning

confidence: 99%

See 1 more Smart Citation

Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

Kim,

Hasan,

Kellogg

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The use of data-driven technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is growing in healthcare. However, the proliferation of healthcare AI tools has outpaced regulatory frameworks, accountability measures, and governance standards to ensure safe, effective, and equitable use. To address these gaps and tackle a common challenge faced by healthcare delivery organizations, a case-based workshop was organized and a framework to assess the potential impact of a new AI solution on health equity was developed. The Health Equity Across the AI Lifecycle (HEAAL) is co-designed with extensive engagement of clinical, operational, technical, and regulatory leaders across healthcare delivery organizations and ecosystem partners in the US. It assesses 5 equity assessment domains–accountability, fairness, fitness for purpose, reliability and validity, and transparency–across the span of 8 key decision points in the AI adoption lifecycle. It is a process-oriented framework containing 37 step-by-step procedures for evaluating an existing AI solution and 34 procedures for evaluating a new AI solution in total. Within each procedure, it identifies relevant key stakeholders and data sources used to conduct the procedure. HEAAL guides how healthcare delivery organizations may mitigate the potential risk of AI solutions worsening health inequities. It also informs how much resources and support are required to assess the potential impact of AI solutions on health inequities.

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Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting

et al. 2023

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Advances in artificial intelligence have cultivated a strong interest in developing and validating the clinical utilities of computer-aided diagnostic models. Machine learning for diagnostic neuroimaging has often been applied to detect psychological and neurological disorders, typically on small-scale datasets or data collected in a research setting. With the collection and collation of an ever-growing number of public datasets that researchers can freely access, much work has been done in adapting machine learning models to classify these neuroimages by diseases such as Alzheimer’s, ADHD, autism, bipolar disorder, and so on. These studies often come with the promise of being implemented clinically, but despite intense interest in this topic in the laboratory, limited progress has been made in clinical implementation. In this review, we analyze challenges specific to the clinical implementation of diagnostic AI models for neuroimaging data, looking at the differences between laboratory and clinical settings, the inherent limitations of diagnostic AI, and the different incentives and skill sets between research institutions, technology companies, and hospitals. These complexities need to be recognized in the translation of diagnostic AI for neuroimaging from the laboratory to the clinic.

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Editorial: Surfacing best practices for AI software development and integration in healthcare

Cited by 4 publications

References 20 publications

Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting

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