Simona Napoli¹, Daniela Maria Capuano^2,3, Roberto Verna^1-3*

¹Research and Training Center for Health and Well-Being, Italy
²Academy for Health and Clinical Research, Italy

*Corresponding Author: Roberto Verna, Research and Training Center for Health and Well-Being, Italy.

Received: June 15, 2026; Published: July 03, 2026

In recent years, clinical research has been undergoing a profound transformation thanks to technological innovation and the increasing digitalization of healthcare processes. In particular, Artificial Intelligence and Digital Health are becoming the key drivers in the evolution of the design, conduct, and analysis of clinical studies. The integration of these technologies promises to make clinical research more efficient and more patient-centered, responding not only to the needs of increasingly complex healthcare systems but also, and above all, to the importance of generating concrete and reliable data in a short time. The use of Artificial Intelligence has proven to be a valuable support for the analysis of large volumes of heterogeneous data; the challenge lies in the enormous amount of data that are constantly generated from different sources and require specific processing to verify their truthfulness and accuracy. Digital Health, on the other hand, encompasses a range of technological solutions: electronic health records, wearable devices, mobile applications, and telemedicine systems that enable remote physician-patient interaction and continuously generate real-time health data. Artificial Intelligence and Digital Health are opening new perspectives for the use of Real World Data and for the real-time monitoring of clinical outcomes. In modern clinical research, these tools are applied in numerous phases of a clinical study’s life cycle: experimental design, patient recruitment, study monitoring, and data analysis. Artificial Intelligence and Digital Health offer innovative solutions that reduce time and costs, improve data quality, and increase patient participation during clinical studies. However, many challenges and critical issues still remain, such as data reliability, the risk of algorithmic bias, and the difficulty of clinically validating the models used. Furthermore, integrating these technologies into regulatory processes and Good Clinical Practice (GCP) requires an adaptation of the professional skills of personnel involved in clinical studies and an evolution of the applied models, as there is a change in processes and organization, a change in mindset, and a change in culture, to which healthcare professionals must adapt; to do so, they must remain informed and properly trained. Another highly relevant aspect concerns the ethical and legal implications of Artificial Intelligence and Digital Health in clinical research. The cornerstone of clinical research lies in protecting patient integrity. The principle of patient centrality requires that every technology or procedure be implemented exclusively under the rigorous supervision of qualified personnel, ensuring that innovation never disregards safety standards and appropriate direct clinical monitoring. Supporting this, European and international regulations ensure the ethical and safe use of new technologies in modern clinical research. Through these considerations, this review aims to analyze the impact of Artificial Intelligence and Digital Health on modern clinical research, with particular attention to their main applications, operational challenges, and ethical aspects. The objective is to provide a comprehensive overview of the potential and limitations of these technologies, highlighting the central role of the researcher in their responsible and informed use, so that clinical research may also align itself with the digital era.

References

1. McCarthy J., et al. “A proposal for the Dartmouth summer research project on artificial intelligence”. AI Magazine4 (2006): 12-14.
2. Topol EJ. “High-performance medicine: The convergence of human and artificial intelligence”. Nature Medicine1 (2019): 44-56.
3. Goldsack JC., et al. “Digital health, digital medicine, digital therapeutics (DTx): What's the difference?” Digital Medicine Society (DiMe) (2019).
4. Steinhubl S R., et al. “The emerging field of mobile health”. Science Translational Medicine 7 (283 (2015): 283rv3.
5. Russell SJ and Norvig P. “Artificial intelligence: A modern approach”. (4^th,) (2021): 15-25.
6. Rajkomar A., et al. “Machine learning in medicine”. The New England Journal of Medicine14 (2019): 1347-1358.
7. Kelly C J., et al. “Key challenges for delivering clinical impact with artificial intelligence”. BMC Medicine 1 (2019): 195.
8. S. Food and Drug Administration (FDA). “Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry”. Silver Spring, MD: U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER Center for Biologics Evaluation and Research (CBER) (2019): 7-27.
9. Izmailova ES., et al. “Wearable devices in clinical trials: Hype and hypothesis”. Clinical Pharmacology and Therapeutics1 (2018): 42-52.
10. Sherman R E., et al. “Real-world evidence—What is it and what can it tell us?” The New England Journal of Medicine23 (2016): 2293-2297.
11. Corrigan-Curay J., et al. “Real-world evidence and real-world data for evaluating drug safety and effectiveness”. JAMA9 (2018): 867-868.
12. Wiens J., et al. “Do no harm: A roadmap for responsible machine learning for health care”. Nature Medicine9 (2019): 1337-1340.
13. Mittelstadt B D., et al. “The ethics of algorithms: Mapping the debate”. Big Data and Society2 (2016): 1-21.
14. Floridi L., et al. “AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations”. Minds and Machines4 (2018): 689-707.
15. World Health Organization. “Ethics and governance of artificial intelligence for health: WHO guidance”. World Health Organization (2021): 35-37.
16. Wager K A., et al. “Health care information systems: A practical approach for health care management”. (4^th,) (2017): 624.
17. European Parliament and Council of the European Union. “Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation)”. Official Journal of the European Union L119 (2016): 35.
18. European Medicines Agency (EMA) & Heads of Medicines Agencies (HMA Reflection paper on the use of Artificial Intelligence (AI) in the medicinal product lifecycle, EMA/CHMP/CVMP/83833/2023, Amsterdam, EMA, (2024): 4-12.
19. S. Food and Drug Administration. “Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD): Discussion paper and request for feedback”. (2019): 1-20.
20. Ghassemi M., et al. “The false hope of current approaches to explainable artificial intelligence in health care”. The Lancet Digital Health11 (2021): e745-e750.

Citation

Citation: Roberto Verna., et al. “The Impact of Artificial Intelligence and Digital Health on Modern Clinical Research: Applications, Challenges, and Ethical Aspects". Acta Scientific Medical Sciences 10.7 (2026): 48-65.

Copyright

Copyright: © 2026 Roberto Verna., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.