- Open Access
- Article
The Dual Impact of AI in Clinical Trials: Perspective
by Muhammad Yaqub 
and Lan He
School of Biomedical Sciences, Hunan University, Changsha, 410000, P.R China
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 9, Page # 16-25, 2024; DOI: 10.55708/js0309002
Keywords: Artificial Intelligence, Clinical Trials, Patient Outcomes, Predictive Analytics, Healthcare Inequities
Received: 07 August 2024, Revised: 05 September 2024, Accepted: 06 September 2023, Published Online: 15 September 2024
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
APA Style
Yaqub, M., & He, L. (2024). The dual impact of AI in clinical trials: Perspective. Journal of Engineering Research and Sciences, 3(9), 16-25. https://doi.org/10.55708/js0309002
Chicago/Turabian Style
Yaqub, Muhammad, and Lan He. “The Dual Impact of AI in Clinical Trials: Perspective.” Journal of Engineering Research and Sciences 3, no. 9 (2024): 16-25. https://doi.org/10.55708/js0309002.
IEEE Style
M. Yaqub and L. He, “The Dual Impact of AI in Clinical Trials: Perspective,” Journal of Engineering Research and Sciences, vol. 3, no. 9, pp. 16-25, 2024, doi: 10.55708/js0309002.
The use of artificial intelligence in clinical trials opens the way for a period of transformation in which the potential to enhance efficiency, precision, and scope in clinical investigation is enormous. In this perspective, the promise and perils of AI regarding clinical trials are critically reviewed. On one hand, it’s where AI can really make some revolutionary changes to the most critical aspects of trial design and execution—like patient recruitment, data management, and predictive analytics—which are now adaptive and more driven by data; on the other hand, AI applied in clinical trials also brings considerable problems related to data integrity, ethical considerations, and regulatory compliance. Such potential of AI to further biases, break patient confidences, and exacerbate already wide inequities in healthcare raises the need for vigilant oversight. As AI is rapidly evolving, so too are principles that guide transparency, fairness, and ethical rigors that guide their application in clinical trials. This work presents a case for strong AI frameworks that are subject to firm validation and ethical scrutiny, which will ensure that the benefits of AI are realized with reduced risks associated with it. If the field goes down this road, AI integration in clinical trials is going to be the real catalyst for innovation, which could translate into achieving better patient outcomes and expanding frontiers for medical science.
- Askin, D. Burkhalter, G. Calado, S. El Dakrouni, Artificial Intelligence Applied to clinical trials: opportunities and challenges, Health and Technology, vol. 13, no. 2, 2023, doi:10.1007/s12553-023-00738-2.
- S. Piantadosi, Clinical trials: a methodologic perspective, John Wiley & Sons, 2024.
- H. Saeed, I. El Naqa, Artificial intelligence in clinical trials, Springer: 453–501, 2022.
- B.C. Stahl, Artificial Intelligence for a Better Future An Ecosystem Perspective on the Ethics of AI and Emerging Digital Technologies, 2021.
- W.H. Organization, Ethics and governance of artificial intelligence for health: large multi-modal models. WHO guidance, World Health Organization, 2024.
- E.J. Topol, High-performance medicine: the convergence of human and artificial intelligence, Nature Medicine, vol. 25, no. 1, 2019, doi:10.1038/s41591-018-0300-7.
- N. Rieke, J. Hancox, W. Li, F. Milletarì, H.R. Roth, S. Albarqouni, S. Bakas, M.N. Galtier, B.A. Landman, K. Maier-Hein, S. Ourselin, M. Sheller, R.M. Summers, A. Trask, D. Xu, M. Baust, M.J. Cardoso, “The future of digital health with federated learning,” Npj Digital Medicine, vol. 3, no. 1, 2020, doi:10.1038/s41746-020-00323-1.
- M. Woo, “Trial by artificial intelligence,” Nature, vol. 573, no. 7775, pp. S100–S102, 2019.
- S.M. McKinney, M. Sieniek, V. Godbole, J. Godwin, N. Antropova, H. Ashrafian, T. Back, M. Chesus, G.C. Corrado, A. Darzi, M. Etemadi, F. Garcia-Vicente, F.J. Gilbert, M. Halling-Brown, D. Hassabis, S. Jansen, A. Karthikesalingam, C.J. Kelly, D. King, J.R. Ledsam, D. Melnick, H. Mostofi, L. Peng, J.J. Reicher, B. Romera-Paredes, R. Sidebottom, M. Suleyman, D. Tse, K.C. Young, et al., “International evaluation of an AI system for breast cancer screening,” Nature, vol. 577, no. 7788, 2020, doi:10.1038/s41586-019-1799-6.
- T. Dhar, N. Dey, S. Borra, R.S. Sherratt, “Challenges of Deep Learning in Medical Image Analysis—Improving Explainability and Trust,” IEEE Transactions on Technology and Society, vol. 4, no. 1, 2023, doi:10.1109/tts.2023.3234203.
- S. Gerke, T. Minssen, G. Cohen, Ethical and legal challenges of artificial intelligence-driven healthcare, 2020, doi:10.1016/B978-0-12-818438-7.00012-5.
- A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau, S. Thrun, “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, 2017, doi:10.1038/nature21056.
- F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, Y. Wang, Artificial intelligence in healthcare: Past, present and future, Stroke and Vascular Neurology, vol. 2, no. 4, 2017, doi:10.1136/svn-2017-000101.
- T.M. Maddox, Clinical Decision Support in Statin Prescription – What We Can Learn from a Negative Outcome, JAMA Cardiology, vol. 6, no. 1, 2021, doi:10.1001/jamacardio.2020.4756.
- N. Fleming, “How artificial intelligence is changing drug discovery,” Nature, vol. 557, no. 7707, 2018, doi:10.1038/d41586-018-05267-x.
- F.Y. Chiu, Y. Yen, Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives, Biomarker Research, vol. 11, no. 1, 2023, doi:10.1186/s40364-023-00476-7.
- M. Yaqub, F. Jinchao, M.S. Zia, K. Arshid, K. Jia, Z.U. Rehman, A. Mehmood, “State-of-the-art CNN optimizer for brain tumor segmentation in magnetic resonance images,” Brain Sciences, vol. 10, no. 7, 2020, doi:10.3390/brainsci10070427.
- K.D. Davis, N. Aghaeepour, A.H. Ahn, M.S. Angst, D. Borsook, A. Brenton, M.E. Burczynski, C. Crean, R. Edwards, B. Gaudilliere, G.W. Hergenroeder, M.J. Iadarola, S. Iyengar, Y. Jiang, J.T. Kong, S. Mackey, C.Y. Saab, C.N. Sang, J. Scholz, M. Segerdahl, I. Tracey, C. Veasley, J. Wang, T.D. Wager, A.D. Wasan, M.A. Pelleymounter, “Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities,” Nature Reviews Neurology, vol. 16, no. 7, 2020, doi:10.1038/s41582-020-0362-2.
- A. Prelaj, V. Miskovic, M. Zanitti, F. Trovo, C. Genova, G. Viscardi, S.E. Rebuzzi, L. Mazzeo, L. Provenzano, S. Kosta, M. Favali, A. Spagnoletti, L. Castelo-Branco, J. Dolezal, A.T. Pearson, G. Lo Russo, C. Proto, M. Ganzinelli, C. Giani, E. Ambrosini, S. Turajlic, L. Au, M. Koopman, S. Delaloge, J.N. Kather, F. de Braud, M.C. Garassino, G. Pentheroudakis, C. Spencer, et al., Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review, Annals of Oncology, vol. 35, no. 1, 2024, doi:10.1016/j.annonc.2023.10.125.
- B. Rossi, B. Santos-Lima, E. Terrabuio, E. Zenaro, G. Constantin, Common Peripheral Immunity Mechanisms in Multiple Sclerosis and Alzheimer’s Disease, Frontiers in Immunology, vol. 12, 2021, doi:10.3389/fimmu.2021.639369.
- E.T. Hébert, C.K. Ra, A.C. Alexander, A. Helt, R. Moisiuc, D.E. Kendzor, D.J. Vidrine, R.K. Funk-Lawler, M.S. Businelle, “A mobile just-in-time adaptive intervention for smoking cessation: Pilot randomized controlled trial,” Journal of Medical Internet Research, vol. 22, no. 3, 2020, doi:10.2196/16907.
- S. Prabhu, K. Prasad, A. Robels-Kelly, X. Lu, AI-based carcinoma detection and classification using histopathological images: A systematic review, Computers in Biology and Medicine, vol. 142, 2022, doi:10.1016/j.compbiomed.2022.105209.
- W.M. van Oostveen, E.C.M. de Lange, “Imaging techniques in Alzheimer’s disease: a review of applications in early diagnosis and longitudinal monitoring,” International Journal of Molecular Sciences, vol. 22, no. 4, 2110, 2021.
- W. He, Z. Zhang, Y. Luo, R.T.K. Kwok, Z. Zhao, B.Z. Tang, Recent advances of aggregation-induced emission materials for fluorescence image-guided surgery, Biomaterials, vol. 288, 2022, doi:10.1016/j.biomaterials.2022.121709.
- A. Smart, P. Martin, M. Parker, Tailored medicine: Whom will it fit? The ethics of patient and disease stratification, Bioethics, vol. 18, no. 4, 2004, doi:10.1111/j.1467-8519.2004.00400.x.
- Z. Chaudhary, C. Lungren, A. Shah, “Tempus: Reshaping the Landscape of Cancer Care with Revolutionary Artificial Intelligence,” Teens in Health AI in Healthcare Summer 2023 Journal Teens in Health Is a Teen Led Organization That Aims to Provide Open Access to Biological Research Skill Development through Researching and Writing Articles, 148, 2023.
- K.H. Zou, C. Vigna, A. Talwai, R. Jain, A. Galaznik, M.L. Berger, J.Z. Li, The Next Horizon of Drug Development: External Control Arms and Innovative Tools to Enrich Clinical Trial Data, Therapeutic Innovation and Regulatory Science, vol. 58, no. 3, 2024, doi:10.1007/s43441-024-00627-4.
- D. Hartl, V. de Luca, A. Kostikova, J. Laramie, S. Kennedy, E. Ferrero, R. Siegel, M. Fink, S. Ahmed, J. Millholland, A. Schuhmacher, M. Hinder, L. Piali, A. Roth, Translational precision medicine: an industry perspective, Journal of Translational Medicine, vol. 19, no. 1, 2021, doi:10.1186/s12967-021-02910-6.
- M. Shanmuga Sundari, H.R. Penthala, A. Mogullapalli, M.M. Ammangatambu, “AI-Based Personalized Drug Treatment,” Artificial Intelligence and Machine Learning in Drug Design and Development, 369–406, 2024.
- J. Geraci, R. Bhargava, B. Qorri, P. Leonchyk, D. Cook, M. Cook, F. Sie, L. Pani, “Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS,” Frontiers in Computational Neuroscience, vol. 17, 2023, doi:10.3389/fncom.2023.1199736.
- S.M. Williamson, V. Prybutok, Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare, Applied Sciences (Switzerland), vol. 14, no. 2, 2024, doi:10.3390/app14020675.
- R. Schwartz, R. Schwartz, A. Vassilev, K. Greene, L. Perine, A. Burt, P. Hall, Towards a standard for identifying and managing bias in artificial intelligence, US Department of Commerce, National Institute of Standards and Technology, 2022.
- N. Hendrix, D.L. Veenstra, M. Cheng, N.C. Anderson, S. Verguet, “Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities,” Value in Health, vol. 25, no. 3, 2022, doi:10.1016/j.jval.2021.08.015.
- A. Jungbauer, G. Ferreira, M. Butler, S. D’Costa, K. Brower, A. Rayat, R. Willson, “Status and future developments for downstream processing of biological products: Perspectives from the Recovery XIX yield roundtable discussions,” Biotechnology and Bioengineering, 2024.
- P.G.R. de Almeida, C.D. dos Santos, J.S. Farias, “Artificial Intelligence Regulation: a framework for governance,” Ethics and Information Technology, vol. 23, no. 3, 2021, doi:10.1007/s10676-021-09593-z.
- A. Nassar, M. Kamal, “Ethical Dilemmas in AI-Powered Decision-Making: A Deep Dive into Big Data-Driven Ethical Considerations,” International Journal of Responsible Artificial Intelligence, vol. 11, no. 8, 2021.
- E. Nasarian, R. Alizadehsani, U.R. Acharya, K.-L. Tsui, “Designing interpretable ML system to enhance trust in healthcare: A systematic review to proposed responsible clinician-AI-collaboration framework,” Information Fusion, 102412, 2024.
