HUMANE: Harmonious Understanding of Machine Learning Analytics Network—global consensus for research on artificial intelligence in medicine Article Swipe
Neha Deo
,
Faisal A. Nawaz
,
Clea du Toit
,
Tran Tran
,
Chaitanya Mamillapalli
,
Piyush Mathur
,
Sandeep Reddy
,
Shyam Visweswaran
,
T. N. Prabhu
,
Khalid Moidu
,
Sandosh Padmanabhan
,
Rahul Kashyap
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.37349/edht.2024.00018
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.37349/edht.2024.00018
Aim: AI research, development, and implementation are expanding at an exponential pace across healthcare. This paradigm shift in healthcare research has led to increased demands for clinical outcomes, all at the expense of a significant gap in AI literacy within the healthcare field. This has further translated to a lack of tools in creating a framework for literature in the AI in medicine domain. We propose HUMANE (Harmonious Understanding of Machine Learning Analytics Network), a checklist for establishing an international consensus for authors and reviewers involved in research focused on artificial intelligence (AI) or machine learning (ML) in medicine. Methods: This study was conducted using the Delphi method by devising a survey using the Google Forms platform. The survey was developed as a checklist containing 8 sections and 56 questions with a 5-point Likert scale. Results: A total of 33 survey respondents were part of the initial Delphi process with the majority (45%) in the 36–45 years age group. The respondents were located across the USA (61%), UK (24%), and Australia (9%) as the top 3 countries, with a pre-dominant healthcare background (42%) as early-career professionals (3–10 years’ experience) (42%). Feedback showed an overall agreeable consensus (mean ranges 4.1–4.8, out of 5) as cumulative scores throughout all sections. The majority of the consensus was agreeable with the Discussion (Other) section of the checklist (median 4.8 (interquartile range (IQR) 4.8-4.8)), whereas the least agreed section was the Ground Truth (Expert(s) review) section (median 4.1 (IQR 3.9–4.2)) and the Methods (Outcomes) section (median 4.1 (IQR 4.1–4.1)) of the checklist. The final checklist after consensus and revision included a total of 8 sections and 50 questions. Conclusions: The HUMANE international consensus has reflected on further research on the potential of this checklist as an established consensus in improving the reliability and quality of research in this field.
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.37349/edht.2024.00018
- OA Status
- diamond
- Cited By
- 1
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4400219558Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.37349/edht.2024.00018Digital Object Identifier
- Title
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HUMANE: Harmonious Understanding of Machine Learning Analytics Network—global consensus for research on artificial intelligence in medicineWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-30Full publication date if available
- Authors
-
Neha Deo, Faisal A. Nawaz, Clea du Toit, Tran Tran, Chaitanya Mamillapalli, Piyush Mathur, Sandeep Reddy, Shyam Visweswaran, T. N. Prabhu, Khalid Moidu, Sandosh Padmanabhan, Rahul KashyapList of authors in order
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https://doi.org/10.37349/edht.2024.00018Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.37349/edht.2024.00018Direct OA link when available
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Analytics, Artificial intelligence, Data science, Computer scienceTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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