Cited by 285Artificial Intelligence in Healthcare and EducationStatistical Methods in Clinical TrialsArtificial Intelligence Applications

The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.

Artificial Intelligence Applied to clinical trials: opportunities and challenges

Open access

Askin, Scott, Burkhalter, Denis, Calado, Gilda, El Dakrouni, Samar·2023·Health and Technology

DOI 10.1007/s12553-023-00738-2PMC PMC9974218

Open this paper in CoChat — free account Open PDF

Abstract

Background Clinical Trials (CTs) remain the foundation of safe and effective drug development. Given the evolving data-driven and personalized medicine approach in healthcare, it is imperative for companies and regulators to utilize tailored Artificial Intelligence (AI) solutions that enable expeditious and streamlined clinical research. In this paper, we identified opportunities, challenges, and potential implications of AI in CTs. Methods Following an extensive search in relevant databases and websites, we gathered publications tackling the use of AI and Machine Learning (ML) in CTs from the past 5 years in the US and Europe, including Regulatory Authorities' documents. Results Documented applications of AI commonly concern the oncology field and are mostly being applied in the area of recruitment. Main opportunities discussed aim to create efficiencies across CT activities, including the ability to reduce sample sizes, improve enrollment and conduct faster, more optimized adaptive CTs. While AI is an area of enthusiastic development, the identified challenges are ethical in nature and relate to data availability, standards, and most importantly, lack of regulatory guidance hindering the acceptance of AI tools in drug development. However, future implications are significant and are anticipated to improve the probability of success, reduce trial burden and overall, speed up research and regulatory approval. Conclusion The use of AI in CTs is in its relative infancy; however, it is a fast-evolving field. As regulators provide more guidance on the acceptability of AI in specific areas, we anticipate the scope of use to broaden and the volume of implementation to increase rapidly.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1007/s12553-023-00738-2

Cites

36 of 36 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228
Artificial intelligence for clinical trial design
Harrer, S., Shah, P., Antony, B., & Hu, J. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.05.005 · Cited by 672
Artificial Intelligence for Drug Toxicity and Safety
Basile, A. O., Yahi, A., & Tatonetti, N. P. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.07.005 · Cited by 401
The role of machine learning in clinical research: transforming the future of evidence generation
Weissler, E. H., Naumann, T., Andersson, T., Ranganath, R., Elemento, O., Luo, Y., Freitag, D. F., Benoit, J. R. A., Hughes, M. C., Khan, F. M., et al. (2021) · Trials · doi:10.1186/s13063-021-05489-x · Cited by 320
Artificial intelligence in drug design
Zhong, F., Xing, J., Li, X., Liu, X., Fu, Z., Xiong, Z., Lu, D., Wu, X., Zhao, J., Tan, X., et al. (2018) · Science China Life Sciences · doi:10.1007/s11427-018-9342-2 · Cited by 293
Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?
Zhavoronkov, A., Vanhaelen, Q., & Oprea, T. I. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1795 · Cited by 159
Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center
Beck, J. T., Rammage, M., Jackson, G. P., Preininger, A. M., Dankwa-Mullan, I., Roebuck, M. C., Torres, A., Holtzen, H., Coverdill, S. E., Williamson, M. P., et al. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.19.00079 · Cited by 127
EliIE: An open-source information extraction system for clinical trial eligibility criteria
Kang, T., Zhang, S., Tang, Y., Hruby, G. W., Rusanov, A., Elhadad, N., & Weng, C. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx019 · Cited by 110
Improving Clinical Trial Participant Prescreening With Artificial Intelligence (AI): A Comparison of the Results of AI-Assisted vs Standard Methods in 3 Oncology Trials
Calaprice, D., Galil, K., Salloum, W., Zariv, A., & Jimenez, B. (2020) · Therapeutic Innovation & Regulatory Science · doi:10.1007/s43441-019-00030-4 · Cited by 89
Accuracy of an Artificial Intelligence System for Cancer Clinical Trial Eligibility Screening: Retrospective Pilot Study
Haddad, T. C., Helgeson, J. M., Pomerleau, K., Preininger, A. M., Roebuck, M. C., Dankwa‐Mullan, I., Jackson, G. P., & Goetz, M. P. (2021) · JMIR Medical Informatics · doi:10.2196/27767 · Cited by 79

Show all 36 references

Research works printed but not linked: 2

Weissler EH, Naumann T, Andersson T, et al. The role of machine learning in clinical research: transforming the future of evidence generation. Trials. 2021;22(1). https://doi.org/10.1186/s13063-021-05489-x. · DOI
European Commission’s Heads of Medicines Agency. Joint HMA/EMA workshop on artificial intelligence in medicines regulation | European Medicines Agency. Published 2021. Accessed October 17., 2021. https://www.ema.europa.eu/en/events/joint-hmaema-workshop-artificial-intelligence-medicines-regulation

Sorted by citations

Items 201–285 of 285

Clinical Trials in the Light of Diversity, Gender Equity, and Inclusion. New Funding Policies
Delgado, M. C. M., Gutiérrez, J. G., & Artigas, A. (2025) · Springer eBooks · doi:10.1007/978-3-031-86286-1_3 · Cited by 1
Artificial intelligence in medical publishing: Peer or pretender?
Chandra, S. (2023) · Journal of Indira Gandhi Institute Of Medical Sciences · doi:10.4103/jigims.jigims_28_23 · Cited by 1
Computer‐Aided Vaccine Design
Sahu, T. K., & Rao, A. R. (2024) · Wiley eBooks · doi:10.1002/9781119896869.ch3 · Cited by 1
Pharma's AI Inflection Point: What Does It Mean for Early Phase Clinical Development?
Issa, A. M. (2025) · Clinical Pharmacology in Drug Development · doi:10.1002/cpdd.1596 · Cited by 1
Leveraging AI Large Language Models for Writing Clinical Trial Proposals in Dermatology: Instrument Validation Study
Hauptman, M., Copley, D., Young, K., Do, T., Durgin, J. S., Yang, A., Chang, J., Billi, A., Nakamura, M., & Tejasvi, T. (2026) · JMIR Dermatology · doi:10.2196/76674
THE ERA OF ARTIFICIAL INTELLIGENCE AND BIG DATA: A PATHWAY TO PHARMA 4.0
(2023) · International Journal of Biology Pharmacy and Allied Sciences · doi:10.31032/ijbpas/2023/12.10.1018
How artificial intelligence is revolutionizing precision medicine and drug discovery?
Yousefi, A., Zandi, A., Shojaeian, F., Marzban, M. R., Tavakol, M. R., Abiri, H., Hojabri, M., Kaviani, G., & Pournezhad, S. (2023) · Iranian Journal of Blood and Cancer · doi:10.61186/ijbc.15.3.42
Artificial Intelligence Can Bring Tens of Trillions of Rubles to the Pharmaceutical Industry
ПОГРЕБНЯК, А. (2023) · Safety and Risk of Pharmacotherapy · doi:10.30895/2312-7821-2023-11-4-367-371
Artificial Intelligence for Drug Toxicity and Safety
Joshi-Barr, S., & Wampole, M. E. (2023) · Springer eBooks · doi:10.1007/978-3-030-73317-9_134-1
Empowering Medical Monitors: AI-Enabled Semantic Parsing for Enhanced Clinical Data Interpretation
Gadiko, A. (2024) · International Journal of Science and Research (IJSR) · doi:10.21275/sr24403035314
AI: Catalyst for Drug Discovery and Development
Nailwal, K., Durgapal, S., Dasauni, K., & Nailwal, T. K. (2024) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-97-1148-2_18
The Dual Impact of AI in Clinical Trials: Perspective
Yaqub, M., & He, L. (2024) · Journal of Engineering Research and Sciences · doi:10.55708/js0309002
Using digital technology to manage chronic noncommunicable diseases
Narain, J. P., & Islam, M. (2024) · International Journal of Noncommunicable Diseases · doi:10.4103/jncd.jncd_81_24
Conceptual Framework for Designing Hippocratic APIs
Rezayat, S., Burmester, G., Ma, H., & Hartmann, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-75872-0_19
Perspective Chapter: Application of Artificial Intelligence in Pharmacovigilance Practices
Gamaleldin, M. M. A., Abdelhalim, S. M. N. S., & Abraham, I. (2024) · Pharmaceutical science. · doi:10.5772/intechopen.1006062 · Cited by 2
Practical training of AI tools in clinical research
Khan, S. M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33584-6.00022-0
AI in clinical research: Transforming methodologies and discoveries
Khan, S. M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33584-6.00009-8
NFPCA-LDC: A Novel Feature Pyramid with Channel Attention-Based Lung Disease Classification Model
Agnihotri, A., & Kohli, N. (2024) · IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies (INSPECT) · doi:10.1109/inspect63485.2024.10895998
A Deep Learning Framework for Causal Inference in Clinical Trial Design: The CURE AI Large Clinicogenomic Foundation ModelPreprint
Weiss, A. D., Fomin, V., Feng, D., Tang, Z., Cai, J., John, B., & Pfister, N. T. (2025) · medRxiv · doi:10.1101/2025.03.06.25323534
Healthcare Chatbots Using Artificial Intelligence and Sentiment Analysis
Lal, M., Neduncheliyan, S., Kaushik, A., & Goswami, A. K. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8009-3.ch013
A Holistic Meta-Analysis of AI Implementation in the Pharmaceutical Product Lifecycle
Mohamad, F. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-1267-3_41
Artificial Intelligence in Pharmaceutical Drug Development-challenges and the Way Forward
Krishnaswami, V., Muruganantham, S., Raja, J., Arthanari, S., Selvaraj, B. R., & Dharmian, J. P. (2025) · Current Artificial Intelligence · doi:10.2174/0129503752359512250325061847
The Use of AI in Pharma: Results from Mapping of Peer-Reviewed and Grey Literature Across the World
Arora, R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-81086-2_2
Clinical Laboratory Parameter–Driven Machine Learning for Participant Selection in Bioequivalence Studies Among Patients With Gastric Cancer: Framework Development and Validation Study
Shon, B., Seong, S. J., Choi, E. J., Gwon, M., Lee, H. W., Park, J., Chung, H. Y., Jeong, S., & Yoon, Y. (2025) · JMIR AI · doi:10.2196/64845 · Cited by 1
AI Will Offer New Opportunities for Transplantation: Are We Ready?
Maggiore, U., Azzi, J. R., Riella, L. V., & Cravedi, P. (2025) · Transplantation Direct · doi:10.1097/txd.0000000000001820
MEDRAIL: MEDical Reinforcement-based Agentic Intelligence for Lifesciences (Preprint)Preprint
Sharma, P. (2025) · doi:10.2196/preprints.78741
Clinical Engineering and Medical Physics: A Synergistic Approach to Patient Safety and Technology Management
Marza, K. H. O., Jadallah, O. A., Karem, M. A. B. S., Karim, Z. F., & Khalaf, M. H. (2025) · Bright Sky Publications eBooks · doi:10.62906/bs.book.395
Evidence-based medicine has reached maturity: Constant adaptation is imperative for its sustained progress
Cokkinos, D. V. (2025) · Journal of Clinical Images and Medical Case Reports · doi:10.52768/2766-7820/3571
Response to letter to the Editor: “Ethnicity in neuro-oncology research: how are we doing and how can we do better?”
Mirza, A. B., Fayez, F., & Ashkan, K. (2025) · Journal of Neuro-Oncology · doi:10.1007/s11060-025-05180-0
AI: Stabilizer or Disruptor of Our Livelihood
Lam, M. L., & Wong, K. W. (2025) · Springer eBooks · doi:10.1007/978-3-031-91493-5_9
Innovative Implementation, Ethical Challenges, and Future Prospects of Artificial Intelligence in Pharmaceuticals
Reshma, S., Rupani, J., Lalitha, M., Gowri, K., Sambathkumar, M., & Suresh, M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2043-4.ch002
Data Science and AI for Transforming R&D
Sood, P., Kaur, G., Kumar, J., Kaur, N., Jangra, N., & Gautam, R. K. (2025) · Wiley eBooks · doi:10.1002/9781394287383.ch3
The impact of artificial intelligence on drug discovery for neuropsychiatric disorders
Agaram Sundaram, V., Saravanan, B., Bhavani Sowndharya, B., Muthu Chinnakannu Marimuthu, M., Munjal, K., & Chopra, H. (2025) · EXCLI journal · doi:10.17179/excli2025-8378 · Cited by 3
Leveraging Artificial Intelligence Large Language Models for Writing Clinical Trial Proposals in Dermatology (Preprint)Preprint
Hauptman, M., Copley, D., Young, K. Z., Do, T. H., Durgin, J. S., Yang, A. F., Chang, J., Billi, A. C., Nakamura, M., & Tejasvi, T. (2025) · doi:10.2196/preprints.76674
Inteligencia artificial en investigación clínica: innovación, regulación y oportunidades para Argentina
Narvaez, D. P., Diaz-Canton, E. A., & Waisberg, F. (2025) · Oncología Clínica · doi:10.56969/oc.v30i2.206
Ethical concerns of generative AI in healthcare applications
Gaur, L., & Abraham, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33124-4.00009-6
AI Fundamentals
Santosh, K., & Roy, D. G. (2025) · Studies in computational intelligence · doi:10.1007/978-981-95-1248-5_3
Seamless Integration of Technology in Pharmaceuticals
Sagar, B. R., Sharma, A. M., Sharma, S., Singh, S., & Sharma, A. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815324457125010013
Integrated artificial intelligence in patient counseling: A comprehensive guide for bioengineers and medical professionals
Dwivedi, A., Yusufi, R., Derashri, A., Sharma, D., Vinchurkar, K., Patel, D., & Suryawanshi, M. V. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-43823-3.00012-1
Challenges in Integration of Computational Approaches with Clinical Practice
Kumari, M., & Nema, S. (2025) · Springer eBooks · doi:10.1007/978-981-96-9346-7_14
Transforming healthcare technologies through the application of artificial intelligence and machine learning in pharmaceutical sciences
Idris, A., Aung, S., Shah, H., & Giribabu, N. (2025) · International Journal of Clinical Medical Research · doi:10.61466/ijcmr3060002
Advances in the Application of Digital Health Technologies in the Management of Patients With Inflammatory Bowel Disease
Yuan, J., & Zhao, D. (2025) · The American Surgeon · doi:10.1177/00031348251413530
Evidence Behind the Automation of Clinical Trial Statistical Programming: A Scoping Review of Technology Adoption, Validation Frameworks, and AI/ML Integration (2020–2025)Preprint
Yan, J., Zhang, J. G., & Tian, T. (2025) · medRxiv · doi:10.64898/2025.12.24.25342988
Artificial Intelligence in Clinical Trials of Drugs
Yingngam, B. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5258-9.ch008
Catalyzing Drug Discovery Using Artificial Intelligence With Traditional Medicine
Taktaz, F., & Ghoran, S. H. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5258-9.ch002
Leveraging Generative AI in Clinical Studies to Improve Efficiency and Quality of Drug Development
Tang, W., Simone, L., Dwivedi, G., Xu, M., Zhu, X., & Chan, J. W. Y. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_25
A neural network framework for selecting real-time video enhancement algorithms on mobile devices
Khan, M., Rahman, M. I., & Ziar, R. A. (2026) · Scientific Reports · doi:10.1038/s41598-026-36099-9
AI technologies for precision and personalized medicine
Kim, S., & Lee, J. W. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34059-8.00009-9
Full-scale indexing and semantic annotation of CT imaging: boosting FAIRness
Ulrich, H., Hendel, R., Bergh, B., & Schreiweis, B. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03378-4
Artificial intelligence detection of endoscopic moderate-to-severe ulcerative colitis: a novel tool to enhance clinical trial recruitment
Grossberg, L. B., Geeganage, G., Mithal, A., Deyhim, T., Santisteban-Silva, A., Papamichael, K., Elamin, S., Cheifetz, A. S., Berzin, T. M., & Rabinowitz, L. G. (2026) · iGIE · doi:10.1016/j.igie.2026.03.001
Applications de l’intelligence artificielle à la découverte de nouveaux traitements contre les maladies auto-immunes
Moingeon, P. E. (2026) · Bulletin de l Académie Nationale de Médecine · doi:10.1016/j.banm.2026.01.021
Ai-Driven Risk-Based Monitoring in Oncology Clinical Trials
Dandu, H., & Singh, S. (2026) · International Conference on Communication, Computing and Electronics Systems (ICCCES) · doi:10.1109/iccces62661.2026.11436612
Integrating Artificial Intelligence, Circulating Tumor DNA, and Real-World Evidence to Optimize Hematologic Clinical Trials: Toward Adaptive and Learning Trial Designs
Mahmoud, A. M., Touti, J. P., Zaidi, S. R., Kodipad, A. A., & Deambrogi, C. (2026) · Cancers · doi:10.3390/cancers18071173
The future of drug discovery and biotechnology by the recent advancements in artificial intelligence
Pourrahmani, H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45526-1.00007-6
Integrating Artificial Intelligence into Neurotherapeutics: A New Frontier in Drug Design and Precision Healthcare
Sahoo, S. K., Tripathy, B., & Khadanga, M. (2026) · Current Bioinformatics · doi:10.2174/0115748936413988251219064552
When evidence meets artificial intelligence
Cruz-Suarez, G. A., Hincapie-Ayala, D., Osorio, F. O., Gómez-Ayala, M. C., Alzate‐Ricaurte, S., Ariza, F., Esteban, S., Jeon, S., & Celi, L. A. (2026) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2026.101472
AI in designing novel cancer treatments
Kumar, P., Umar, M., Naeem, A., & Khan, E. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45004-4.00011-6
Revolutionizing genomic medicine with artificial intelligence and data analytics
Patra, P. P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27638-5.00013-4
AI-Driven Medicine: Transformations in Diagnostic Methods, Personalized Treatments and Ethical Standards
Saxena, G., & Jani, M. (2026) · Synthesis lectures on computer science · doi:10.1007/978-3-032-18322-4_7
Artificial intelligence
Morales-Guerrero, O. J. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34264-6.00027-7
ToxiGenFormer-StructToxiNet synergy: advancing gender-specific toxicity profiling and molecular substructure insights
Sardana, A., & Goyal, L. M. (2026) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-026-07802-6
Transforming cutting-edge healthcare: Emerging trends in metabolomics and drug design using artificial intelligence and big data methodologies
Kim, S.-J., Kim, D., Ko, J., Zan, X., Wong, K. L., Mao, Y., Kaur, L., 남현준, & Lee, J. W. (2026) · Digital Health · doi:10.1177/20552076261458965
AI-Driven Biostatistics in Healthcare
Jose, A., Mohan, R., & Kaithathara, S. T. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2373-0.ch005
Pharmacological Targeting of Angiogenesis in Head and Neck Cancer: Molecular Mechanisms and Emerging Therapeutic Strategies
Szekely, D., Armega-Anghelescu, A., Barb, A. C., Novacescu, D., Dumitru, C., Manole, A., Dan, R. G., & Baderca, F. (2026) · Pharmaceuticals · doi:10.3390/ph19060950
Ethical, legal, and regulatory perspectives in computational drug design
Rustagi, V., Gupta, S. R. R., Singh, A. K., & Singh, I. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-49253-2.00011-8
Deep learning for clinical research: Current issues and future recommendations
Iqbal, M. S., Sadaf, Hayat, M. A. B., Sayeed, E., Akhtar, S., Qadri, S. F., Shah, R. K., & Akhtar, F. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44021-2.00001-4
Artificial Intelligence-Driven Precision Farming Systems for Optimizing Crop Yield
Ragavi, P., Kesavardhini, K., Banu, A. S., Vijayalakshmi, R., Sandhiya, K., & Saranraj, P. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2691-5.ch009
A Scalable Machine Learning Architecture on SAP BTP for AI-Augmented Clinical Trial Optimization and Adaptive Patient Cohort Stratification in Pharmaceutical Research
Yeruva, V. V. R. (2026) · International conference Computer Networks and Inventive Communication Technologies · doi:10.1109/iccnct68477.2026.11590544
Invited Commentary: Transforming Occupational Therapy Research With Artificial Intelligence
Wolpert, R., Liew, S., Pickett, K. A., & Wu, C.-Y. (2026) · OTJR Occupational Therapy Journal of Research · doi:10.1177/15394492261464096
Artificial Intelligence Across the Vaccine Clinical Trial Lifecycle: Evidence, Readiness, and Guardrails
Idriss, J., Kalash, S., Faraj, J. A., Nolan, L., Bechnak, K., & Rouphael, N. (2026) · Clinical Infectious Diseases · doi:10.1093/cid/ciag445
PRACTALL 2025: Artificial intelligence—application of allergy and immunology to patient care
Shamji, M. H., Rider, N. L., Adcock, I. M., Pongdee, T., Giacco, S. D., Chang, C., Jaen, M. T., & Williams, P. V. (2026) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2026.04.031 · Cited by 1
Data Stewardship Barriers to Building Digital Twin Technology for Precision Medicine
Silva, P. J., Tao, J., Rogers, S. L., He, Q., Robert, J. D., Black, L., Bruce, S. A., Shireman, P. K., & Ramos, K. S. (2026) · AI in Medicine · doi:10.3390/aimed1030020
Industry Practitioners Developing or Implementing AI Platforms for Healthcare and Life Sciences
Atul Kumar Dnyaan Prasad Global University, Pulatov, M., Rejabbayevich, N. D., Almat, S., Рустамович, Х. Т., & Mardonovich, N. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8904-2.ch017
Reassessing R&D outcomes of leading pharmaceutical companies
Schuhmacher, A., Witting, M., Hartl, D., Hinder, M., & Gassmann, O. (2026) · Drug Discovery Today · doi:10.1016/j.drudis.2026.104763
Regulatory Research Priorities for AI Use in the Medicine Lifecycle: A European Perspective with Global Relevance
Pinheiro, L. C., Langston, A., Orre, M., Zinserling, J., Boumaki, K., Hornung, B., O’Sullivan, S., Westman, G., Broich, K., & Arlett, P. R. (2026) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.70400 · Cited by 1
Site Selection for High-Grade Glioma Clinical Drug Trials Using Multi-Criteria Decision Analysis
Liu, F., Cui, X., Dong, C., Zhan, K., Lin, H. M., & Wang, Y. (2026) · International Conference on Bioinformatics and Intelligent Computing (BIC) · doi:10.1145/3809986.3810075
New thinking for the next generation of antimalarials
Asiki, H., Hlozek, J., Wicht, K. J., Woodland, J. G., & Chibale, K. (2026) · EMBO Molecular Medicine · doi:10.1038/s44321-026-00515-4
Artificial Intelligence and Digital Workflow in Craniofacial Bone Tissue Engineering: From Cone-Beam Computed Tomography (CBCT) to Personalized Bioceramic Implants
Bahador, A., Moghaddam, S. A. M., Mojtahedi, H., Hakim, L. K., & Tebyanian, H. (2026) · Dentistry Journal · doi:10.3390/dj14090558
Representative Biobanks as Infrastructure for Equitable Precision Medicine: Implications for Clinical Trials, Artificial Intelligence, and Translational Research
Davis, M., Reimold, C., Taylor, K., & Chakraborty, A. (2026) · Biopreservation and Biobanking · doi:10.1177/19475535261475467
The potential of AI for adjunctive supportive care in cancer survivorship: opportunities, challenges, and the PRECISION-AI practical framework
Penedo, F. J., Fleszar‐Pavlović, S. E., Natori, A., Wang, A., Stathias, V., Bonnell, J. T., Esquives, B. S. N., Crane, T. E., MacIntyre, J., Calfa, C. J., et al. (2026) · Translational Behavioral Medicine · doi:10.1093/tbm/ibag063
Global research trends in interventional bronchoscopy: a bibliometric analysis of publications from 2005 to 2025
Ahmad, T., Ahmad, T., Mustaan, F., Rahman, J. U., & Esa, M. (2026) · Egyptian Journal of Bronchology · doi:10.1186/s43168-026-00687-0
Unlocking funding for human-relevant biomedical research: Navigating the landscape of nonanimal approaches
Hvitved, A. N., & Pistollato, F. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-26514-3.00005-9
Transforming Patient Recruitment and Retention in Clinical Trials with Artificial Intelligence: A Review and Comparative Study
Majid, Z., Chaudhuri, W., & Kanabar, V. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-33123-6_16
How are clinical and translational research professionals using AI? Polling and discussion insights from two ACTS special interest groups
Hendricks‐Sturrup, R., Wells, S. M., & Fessel, J. P. (2026) · Journal of Clinical and Translational Science · doi:10.1017/cts.2026.10844
Fundamental Rights Impact Assessment and the Rule of Law: Focus on High-Risk AI Systems Using Clinical Data
Colcelli, V. (2026) · Springer eBooks · doi:10.1007/978-3-031-94909-8_42

Authors: Askin, S., Burkhalter, D., Calado, G., & El Dakrouni, S.
Affiliations: Novartis (Switzerland) (CH)
Massachusetts College of Pharmacy and Health Sciences (US)
Corresponding author: Askin, Scott
Venue: Health and Technology
Publisher: Springer Science and Business Media LLC
Published: 2023-02-28
Volume: 13
Issue: 2
Pages: 203-213
ISSN: 2190-7188 (ISSN-L)
2190-7196
DOI: 10.1007/s12553-023-00738-2
PMC: PMC9974218
OpenAlex: W4322621757
Cited by: 285
References: 36
Citations by year: 2026: 61 · 2025: 123 · 2024: 83 · 2023: 18
FWCI: 3.35
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Health and Technology (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords: artificial intelligence in clinical trials, machine learning, adaptive clinical trials, clinical trial recruitment, cancer clinical trials, ethical challenges of AI, data availability, drug development, personalized medicine, trial efficiency

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Artificial Intelligence Applied to clinical trials: opportunities and challenges

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks