Table Info

Table 3.

Checklist sections and their cumulative score

Checklist sections	Mean (± SD)	Median (25-75% IQR)
Section 1: Title	4.4 (± 0.21)	4.4 (4.3–4.4)
Section 2: Abstract	4.5*	4.5 (4.5–4.5)
Section 3: Introduction	4.4 (± 0.21)	4.3 (4.3–4.5)
Section 4a: Methods (Data Source)	4.4 (± 0.17)	4.3 (4.2–4.4)
Section 4b: Methods (Participants)	4.4 (± 0.13)	4.4 (4.4–4.5)
Section 4c: Methods (Outcomes)	4.2 (± 0.16)	4.1 (4.1–4.1)
Section 4d: Methods (Statistical Analysis)	4.3 (± 0.16)	4.3 (4.2–4.4)
Section 5a: Ground Truth (Labels)	4.2 (± 0.12)	4.2 (4.2–4.2)
Section 5b: Ground Truth (Expert(s) Review)	4.0 (± 0.20)	4.1 (3.9–4.2)
Section 6: Results	4.2 (± 0.21)	4.2 (4.0–4.3)
Section 7: Discussion	4.2 (± 0.35)	4.3 (4.1–4.4)
7: Discussion (Other)	4.8*	4.8 (4.8–4.8)

* Standard deviation unable to be calculated due to only having a single question in the section. IQR: interquartile range

Supplementary materials

The supplementary tables for this article are available at: https://www.explorationpub.com/uploads/Article/file/101118_sup_1.pdf.

Declarations

Author contributions

ND and FAN equally contributed to: Conceptualization, Investigation, Writing—original draft, Writing—review & editing. CT and TT: Investigation, Formal analysis, Writing—review & editing. CM, PM, SR, SV, TP, KM, and SP: Validation, Writing—review & editing, Supervision. RK: Conceptualization, Investigation, Validation, Writing—review & editing, Supervision. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

All participants in this study agree to participate.

Consent to publication

All participants in this study agree to publish their personal information.

Availability of data and materials

The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.

Funding

Not applicable.

Copyright

Publisher’s note

Open Exploration maintains a neutral stance regarding jurisdictional claims in published maps and institutional affiliations.

References

Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36–40. [DOI] [PubMed]

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. [DOI] [PubMed]

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. [DOI] [PubMed]

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. [DOI] [PubMed]

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. [DOI] [PubMed] [PMC]

AI: Healthcare’s new nervous system [Internet]. Accenture; c2024 [cited 2023 Aug 8]. Available from: https://www.accenture.com/au-en/insights/health/artificial-intelligence-healthcare

How AI Can Change the Future of Health Care [Internet]. Harvard Business School Publishing; c2024 [cited 2023 Aug 8]. Available from: https://hbr.org/webinar/2019/02/how-ai-can-change-the-future-of-health-care

Naudé W. Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. AI Soc. 2020;35:761–5. [DOI] [PubMed] [PMC]

Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020;369:m1328. [DOI] [PubMed] [PMC]

10.

Markus AF, Kors JA, Rijnbeek PR. The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. J Biomed Inform. 2021;113:103655. [DOI] [PubMed]

11.

Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ. 2020;370:m3210. [DOI] [PubMed] [PMC]

12.

Buvat I, Orlhac F. The T.R.U.E. Checklist for Identifying Impactful Artificial Intelligence–Based Findings in Nuclear Medicine: Is It True? J Nucl Med. 2021;62:752–4. [DOI] [PubMed]

13.

Mongan J, Moy L, Kahn CE Jr. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiol Artif Intell. 2020;2:e200029. [DOI] [PubMed] [PMC]

14.

Adadi A, BerradaM. (2020). Explainable AI for Healthcare: From Black Box to Interpretable Models. In: Bhateja V, Satapathy S, Satori H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. [DOI]

15.

Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration. Ann Intern Med. 2015;162:W1–73. [DOI] [PubMed]

16.

Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594. [DOI] [PubMed]

17.

Kwong JCC, McLoughlin LC, Haider M, Goldenberg MG, Erdman L, Rickard M, et al. Standardized Reporting of Machine Learning Applications in Urology: The STREAM-URO Framework. Eur Urol Focus. 2021;7:672–82. [DOI] [PubMed]

18.

Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, et al. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View. J Med Internet Res. 2016;18:e323. [DOI] [PubMed] [PMC]

19.

Mateen BA, Liley J, Denniston AK, Holmes CC, Vollmer SJ. Improving the quality of machine learning in health applications and clinical research. Nat Mach Intell. 2020;2:554–6. [DOI]

20.

Angehrn Z, Haldna L, Zandvliet AS, Gil Berglund E, Zeeuw J, Amzal B, et al. Artificial Intelligence and Machine Learning Applied at the Point of Care. Front Pharmacol. 2020;11:759. [DOI] [PubMed] [PMC]

21.

Gubbi S, Hamet P, Tremblay J, Koch CA, Hannah-Shmouni F. Artificial Intelligence and Machine Learning in Endocrinology and Metabolism: The Dawn of a New Era. Front Endocrinol (Lausanne). 2019;10:185. [DOI] [PubMed] [PMC]

22.

Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care — Addressing Ethical Challenges. N Engl J Med. 2018;378:981–3. [DOI] [PubMed] [PMC]

23.

Du Toit C, Tran T, Aryal S, Lip S, Manandhar I, Sykes R, et al. Investigating the quality of machine learning research and reporting in hypertension. J Hypertens. 2022;40:e78. [DOI]

24.

du Toit C, Tran TQB, Deo N, Aryal S, Lip S, Sykes R, et al. Survey and Evaluation of Hypertension Machine Learning Research. J Am Heart Assoc. 2023;12:e027896. [DOI] [PubMed] [PMC]