Beyond technological enthusiasm
Preserving evidence in accelerated science
DOI:
https://doi.org/10.67463/ynd16543Keywords:
digital health, advanced biomaterials, editorial responsibility, methodological rigor, accountable innovation, robotic telesurgeryAbstract
World Health Organization. Global strategy on digital health 2020-2025. Geneva: World Health Organization; 2021.
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195. doi:10.1186/s12916-019-1426-2.
Goldsack JC, Coravos A, Bakker JP, Bent B, Dowling AV, Fitzer-Attas C, et al. Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for biometric monitoring technologies (BioMeTs). npj Digit Med. 2020;3:55. doi:10.1038/s41746-020-0260-4.
Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. BMJ. 2020;370:m3164. doi:10.1136/bmj.m3164.
Cruz Rivera S, 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:10.1136/bmj.m3210.
Collins GS, Moons KGM, Dhiman P, Logullo P, Beam AL, Peng L, 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.
Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018. doi:10.1038/sdata.2016.18.
National Academies of Sciences, Engineering, and Medicine. Reproducibility and replicability in science. Washington (DC): National Academies Press; 2019. doi:10.17226/25303.
U.S. Food and Drug Administration, Health Canada, Medicines and Healthcare products Regulatory Agency. Good machine learning practice for medical device development: guiding principles. Silver Spring (MD): U.S. Food and Drug Administration; 2021.
U.S. Food and Drug Administration, Health Canada, Medicines and Healthcare products Regulatory Agency. Transparency for machine learning-enabled medical devices: guiding principles. Silver Spring (MD): U.S. Food and Drug Administration; 2024.
Ratner BD, Hoffman AS, Schoen FJ, Lemons JE, editors. Biomaterials science: an introduction to materials in medicine. 4th ed. London: Elsevier; 2020.
Roach P, Farrar D, Perry CC. Interpretation of protein adsorption: surface-induced conformational changes. J Am Chem Soc. 2005;127(22):8168-73. doi:10.1021/ja042898o.
International Organization for Standardization. ISO 10993-1:2018. Biological evaluation of medical devices - Part 1: Evaluation and testing within a risk management process. Geneva: International Organization for Standardization; 2018.
D'Alton L, Souto DEP, Punyadeera C, Abbey B, Voelcker NH, Hogan C, et al. A holistic pathway to biosensor translation. Sens Diagn. 2024;3(8):1234-46. doi:10.1039/D4SD00088A.
de Farias FAC, Dagostini CM, Bicca YA, Falavigna VF, Falavigna A. Remote patient monitoring: a systematic review. Telemed J E Health. 2020;26(5):576-83. doi:10.1089/tmj.2019.0066.
References
World Health Organization. Global strategy on digital health 2020-2025. Geneva: World Health Organization; 2021.
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195. doi:10.1186/s12916-019-1426-2.
Goldsack JC, Coravos A, Bakker JP, Bent B, Dowling AV, Fitzer-Attas C, et al. Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for biometric monitoring technologies (BioMeTs). NPJ Digit Med. 2020;3:55. doi:10.1038/s41746-020-0260-4.
Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; SPIRIT-AI and CONSORT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. BMJ. 2020;370:m3164. doi:10.1136/bmj.m3164.
Cruz Rivera S, 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:10.1136/bmj.m3210.
Collins GS, Moons KGM, Dhiman P, Logullo P, Beam AL, Peng L, 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.
Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018. doi:10.1038/sdata.2016.18.
National Academies of Sciences, Engineering, and Medicine. Reproducibility and replicability in science. Washington (DC): National Academies Press; 2019. doi:10.17226/25303.
U.S. Food and Drug Administration, Health Canada, Medicines and Healthcare products Regulatory Agency. Good machine learning practice for medical device development: guiding principles. Silver Spring (MD): U.S. Food and Drug Administration; 2021.
U.S. Food and Drug Administration, Health Canada, Medicines and Healthcare products Regulatory Agency. Transparency for machine learning-enabled medical devices: guiding principles. Silver Spring (MD): U.S. Food and Drug Administration; 2024.
Wagner WR, Sakiyama-Elbert SE, Zhang G, Yaszemski MJ, editors. Biomaterials science: an introduction to materials in medicine. 4th ed. London: Academic Press; 2020. doi:10.1016/C2017-0-02323-6.
Roach P, Farrar D, Perry CC. Interpretation of protein adsorption: surface-induced conformational changes. J Am Chem Soc. 2005;127(22):8168-73. doi:10.1021/ja042898o.
International Organization for Standardization. ISO 10993-1:2018. Biological evaluation of medical devices—Part 1: evaluation and testing within a risk management process. Geneva: International Organization for Standardization; 2018.
D’Alton L, Souto DEP, Punyadeera C, Abbey B, Voelcker NH, Hogan C, et al. A holistic pathway to biosensor translation. Sens Diagn. 2024;3:1234-46. doi:10.1039/D4SD00088A.
de Farias FAC, Dagostini CM, Bicca YA, Falavigna VF, Falavigna A. Remote patient monitoring: a systematic review. Telemed J E Health. 2020;26(5):576-83. doi:10.1089/tmj.2019.0066.
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Copyright (c) 2026 Lisandro Gonçalves, DDS, MSc
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