Published August 7, 2024
| Version v1
Journal article
Open
Artificial Intelligence and ways to Report it in Pediatric Dental Research: A Review of Recommendations
Creators
Description
The rise of artificial intelligence in evidence-based healthcare in the past two decades calls for a streamlining of guidelines on the reporting of these studies. Conventional reporting guidelines based on various study designs are comprehensively listed by the
Equator-Network. This review presents an overview of reporting guidelines for studies in pediatric dentistry that utilize AI and that are recommended by the Equator-Network.
Files
AI Reporting Guidelines.pdf
Files
(249.1 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:ad0a3f7875c1ffe4be8ce7b9bfd9134c
|
249.1 kB | Preview Download |
Additional details
Software
- Repository URL
- https://archive.org/details/ai-reporting-guidelines
References
- Babu A, Onesimu JA, Sagayam KM. Artificial Intelligence in dentistry: Concepts, Applications and Research Challenges. E3S Web of Conferences [Internet]. 2021;297(01074):1–9. Available from: https://www.e3sconferences. org/articles/e3sconf/abs/2021/73/e3sconf_iccsre21_01074/e3sconf_iccsre 21_01074.html
- Salvatore La Rosa, Quinzi V, Palazzo G, Ronsivalle V, Antonino Lo Giudice. The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence- Based Literature. Healthcare. 2024 Jun 30;12(13):1311–1.
- Equator-Network. Artificial intelligence/Machine Learning Studies | Study Designs | EQUATOR Network [Internet]. Equator-network.org. University of Oxford; 2024 [cited 2024 Jun 2]. Available from: https://www.equatornetwork. org/?post_type=eq_guidelines&eq_guidelines_study_design=artificialintelligence- machine-learningstudies& eq_guidelines_clinical_specialty=0&eq_guidelines_report_section=0&s=&bt n_submit=Search+Reporting+Guidelines
- Snell KIE, Levis B, Damen JAA, Dhiman P, Debray TPA, Hooft L, Reitsma JB, Moons KGM, Collins GS, Riley RD. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ. 2023 May 3;381:e073538. doi: 10.1136/bmj-2022-073538
- Collins G S, Moons K G M, Dhiman P, Riley R D, Beam A L, Van Calster B et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods BMJ 2024; 385 :e078378 doi:10.1136/bmj-2023-078378
- Debray TPA, Collins GS, Riley RD, Snell KIE, Van Calster B, Reitsma JB, Moons KGM. Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist. BMJ. 2023 Feb 7;380:e071018. doi: 10.1136/bmj-2022-071018.
- Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORTAI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ. 2020 Sep 9;370:m3210. doi: 10.1136/bmj.m3210.
- Ibrahim H, Liu X, Rivera SC, Moher D, Chan AW, Sydes MR, Calvert MJ, Denniston AK. Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines. Trials. 2021 Jan 6;22(1):11. doi: 10.1186/s13063-020-04951-6.
- Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M, Liu X, Mateen BA, Mathur P, McCradden MD, Morgan L, Ordish J, Rogers C, Saria S, Ting DSW, Watkinson P, Weber W, Wheatstone P, McCulloch P; DECIDE-AI expert group. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ. 2022 May 18;377:e070904. doi: 10.1136/bmj-2022- 070904.