HUMANE: Harmonious Understanding of Machine Learning Analytics Network—global consensus for research on artificial intelligence in medicine-Reference-Cited by-同舟云学术

HUMANE: Harmonious Understanding of Machine Learning Analytics Network—global consensus for research on artificial intelligence in medicine

Published:2024-06-30 Issue:3 Volume:2 Page:157-166
ISSN:
Container-title:Exploration of Digital Health Technologies
language:
Short-container-title:Explor Digit Health Technol

Author:

Deo Neha¹,Nawaz Faisal A.²^ORCID,du Toit Clea³^ORCID,Tran Tran³,Mamillapalli Chaitanya⁴^ORCID,Mathur Piyush⁵^ORCID,Reddy Sandeep⁶^ORCID,Visweswaran Shyam⁷^ORCID,Prabhu Thanga⁸,Moidu Khalid⁹^ORCID,Padmanabhan Sandosh³^ORCID,Kashyap Rahul¹⁰^ORCID

Affiliation:

1. Massachusetts General Hospital, Boston, MA 02114, USA

2. Al Amal Psychiatric Hospital, Emirates Health Services, Dubai 2299, United Arab Emirates

3. School of Cardiovascular and Metabolic Health, University of Glasgow, G12 8TA Glasgow, UK

4. Department of Endocrinology, Springfield Clinic, Springfield, IL 62702-5104, USA

5. Department of Anesthesiology, Cleveland Clinic, Cleveland, OH 44195, USA

6. Chair, Healthcare Operations, Deakin University, Geelong, VIC 3216, Australia

7. Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206-3701, USA

8. Chief Medical Information Officer, Apollo Hospitals, Chennai 600006, TN, India

9. Chief Information Officer Consultant, Orlando, FL, USA

10. Department of Anesthesiology and Critical Care Medicine, Mayo Clinic, Rochester, MN 55905, USA; Department of Research, WellSpan Health, York, PA 17403, USA

Abstract

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.

Publisher

Open Exploration Publishing

Reference24 articles.

1. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36–40.

2. Bellemo V, Lim G, Rim TH, Tan GSW, Cheung CY, Sadda S, et al. Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application. Curr Diab Rep. 2019;19:72.

3. Xiang Y, Zhao L, Liu Z, Wu X, Chen J, Long E, et al. Implementation of artificial intelligence in medicine: Status analysis and development suggestions. Artif Intell Med. 2020;102:101780.

4. De Fauw J, Ledsam JR, Romera-Paredes B, Nikolov S, Tomasev N, Blackwell S, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med. 2018;24:1342–50.

5. Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018;2:35.