From physicochemical reality to reliable digital evidence

Physicochemical foundations for translational research

Authors

DOI:

https://doi.org/10.67463/881bbn65

Keywords:

digital health, advanced biomaterials, translational research, methodological rigor, digital evidence

Abstract

This inaugural editorial establishes the scientific and editorial foundation of the Journal of Digital Health and Advanced Biomaterials (JDHAB). It defines the journal’s central premise that credible digital claims in health must remain accountable to material reality, measurement quality, methodological transparency, and reproducible documentation. The editorial outlines JDHAB’s scope at the interface of digital health and advanced biomaterials and affirms the journal’s commitment to analytical rigor, editorial integrity, traceability, and translational relevance.

Author Biography

  • Lisandro Gonçalves, Journal of Digital Health and Advanced Biomaterials (JDHAB), Maringá, Paraná, Brazil; MSc Program in Endodontics, University of Ribeirão Preto, São Paulo, Brazil

    Editor-in-Chief, Journal of Digital Health and Advanced Biomaterials (JDHAB), Maringá, Paraná, Brazil. MSc Program in Endodontics, University of Ribeirão Preto, São Paulo, Brazil.

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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/10.1016/B978-0-12-816137-1.00001-5

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. DOI: https://doi.org/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. DOI: https://doi.org/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. DOI: https://doi.org/10.1089/tmj.2019.0066

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Published

2026-03-07

Issue

Section

Editorial

How to Cite

1.
Gonçalves L. From physicochemical reality to reliable digital evidence: Physicochemical foundations for translational research. J Digit Health Adv Biomater [Internet]. 2026 Mar. 7 [cited 2026 Jul. 2];1(1):1–4. Available from: https://test.journal.jdhab.org/index.php/jhab/article/view/editorial-physicochemical-reality-digital-evidence