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Lu X, Chen M, Lu Z, Shi X, Liang L. Artificial intelligence tools for optimising recruitment and retention in clinical trials: a scoping review protocol. BMJ Open 2024; 14:e080032. [PMID: 38508642 PMCID: PMC10953313 DOI: 10.1136/bmjopen-2023-080032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/29/2024] [Indexed: 03/22/2024] Open
Abstract
INTRODUCTION In recent years, the influence of artificial intelligence technology on clinical trials has been steadily increasing. It has brought about significant improvements in the efficiency and cost reduction of clinical trials. The objective of this scoping review is to systematically map, describe and summarise the current utilisation of artificial intelligence in recruitment and retention process of clinical trials that has been reported in research. Additionally, the review aims to identify benefits and drawbacks, as well as barriers and facilitators associated with the application of artificial intelligence in optimising recruitment and retention in clinical trials. The findings of this review will provide insights and recommendations for future development of artificial intelligence in the context of clinical trials. METHODS AND ANALYSIS The review of relevant literature will follow the methodological framework for scoping studies provided by the Joanna Briggs Institute. A comprehensive electronic search will be conducted using the search strategy developed by the authors. Leading medical and computer science databases such as PubMed, Embase, Scopus, IEEE Xplore and Web of Science Core Collection will be searched. The search will encompass analytical observational studies, descriptive observational studies, experimental and quasi-experimental studies published in all languages, without any time limitations, which use artificial intelligence tools in the recruitment and retention process of clinical trials. The review team will screen the identified studies and import them into a dedicated electronic library specifically created for this review. Data extraction will be performed using a data charting table. ETHICS AND DISSEMINATION Secondary data will be attained in this scoping review; therefore, no ethical approval is required. The results of the final review will be published in a peer-reviewed journal. It is expected that results will inform future artificial intelligence and clinical trials research.
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Affiliation(s)
- Xiaoran Lu
- School of Humanities, Central South University, Changsha, People's Republic of China
- University of Liverpool Faculty of Arts, Liverpool, UK
| | - Mingan Chen
- School of Humanities, Central South University, Changsha, People's Republic of China
| | - Zhuolin Lu
- School of Humanities, Central South University, Changsha, People's Republic of China
| | - Xiaoting Shi
- Department of Environmental Health Sciences, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Lu Liang
- School of Humanities, Central South University, Changsha, People's Republic of China
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Nag S, Baidya ATK, Mandal A, Mathew AT, Das B, Devi B, Kumar R. Deep learning tools for advancing drug discovery and development. 3 Biotech 2022; 12:110. [PMID: 35433167 PMCID: PMC8994527 DOI: 10.1007/s13205-022-03165-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 03/18/2022] [Indexed: 12/26/2022] Open
Abstract
A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug–target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.
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Affiliation(s)
- Sagorika Nag
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Anurag T. K. Baidya
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Abhimanyu Mandal
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Alen T. Mathew
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Bhanuranjan Das
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Bharti Devi
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
| | - Rajnish Kumar
- Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (B.H.U.), Varanasi, UP 221005 India
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Kharbat FF, Alshawabkeh A, Woolsey ML. Identifying gaps in using artificial intelligence to support students with intellectual disabilities from education and health perspectives. ASLIB J INFORM MANAG 2020. [DOI: 10.1108/ajim-02-2020-0054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeStudents with developmental/intellectual disabilities (ID/DD) often have serious health issues that require additional medical care and supervision. Serious health issues also mean increased absence and additional lags in academic achievement and development of adaptive and social skills. The incorporation of artificial intelligence in the education of a child with ID/DD could ameliorate the educational, adaptive and social skill gaps that occur as a direct result of persistent health problems.Design/methodology/approachThe literature regarding the use of artificial intelligence in education for students with ID/DD was collected systematically from international online databases based on specific inclusion and exclusion criteria. The collected articles were analyzed deductively, looking for the different gaps in the domain. Based on the literature, an artificial intelligence–based architecture is proposed and sketched.FindingsThe findings show that there are many gaps in supporting students with ID/DD through the utilization of artificial intelligence. Given that the majority of students with ID/DD often have serious and chronic and comorbid health conditions, the potential use of health information in artificial intelligence is even more critical. Therefore, there is a clear need to develop a system that facilitates communication and access to health information for students with ID/DD, one that provides information to caregivers and education providers, limits errors, and, therefore, improves these individuals' education and quality of life.Practical implicationsThis review highlights the gap in the current literature regarding using artificial intelligence in supporting the education of students with ID/DD. There is an urgent need for an intelligent system in collaboration with the updated health information to improve the quality of services submitted for people with intellectual disabilities and as a result improving their quality of life.Originality/valueThis study contributes to the literature by highlighting the gaps in incorporating artificial intelligence and its service to individuals with ID/DD. The research additionally proposes a solution based on the confounding variables of students’ health and individual characteristics. This solution will provide an automated information flow as a functional diagnostic and intervention tool for teachers, caregivers and parents. It could potentially improve the educational and practical outcomes for individuals with ID/DD and, ultimately, their quality of life.
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Tran BX, Nghiem S, Sahin O, Vu TM, Ha GH, Vu GT, Pham HQ, Do HT, Latkin CA, Tam W, Ho CSH, Ho RCM. Modeling Research Topics for Artificial Intelligence Applications in Medicine: Latent Dirichlet Allocation Application Study. J Med Internet Res 2019; 21:e15511. [PMID: 31682577 PMCID: PMC6858616 DOI: 10.2196/15511] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 08/28/2019] [Accepted: 08/28/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI)-based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and research landscape of AI in this field. OBJECTIVE This study aimed to evaluate the global development of scientific publications and constructed interdisciplinary research topics on the theory and practice of AI in medicine from 1977 to 2018. METHODS We obtained bibliographic data and abstract contents of publications published between 1977 and 2018 from the Web of Science database. A total of 27,451 eligible articles were analyzed. Research topics were classified by latent Dirichlet allocation, and principal component analysis was used to identify the construct of the research landscape. RESULTS The applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). However, AI applications have not been commonly used in resource-poor settings due to the limit in infrastructure and human resources. CONCLUSIONS The application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies. AI might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries. Technology transfer and support from developed countries are essential measures for the advancement of AI application in health care in developing countries.
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Affiliation(s)
- Bach Xuan Tran
- Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam.,Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Son Nghiem
- Centre for Applied Health Economics, Griffith University, Brisbane, Australia
| | - Oz Sahin
- Griffith Climate Change Response Program, Griffith University, Brisbane, Australia
| | - Tuan Manh Vu
- Odonto Stomatology Research Center for Applied Science and Technology, Hanoi Medical University, Hanoi, Vietnam
| | - Giang Hai Ha
- Institute for Global Health Innovations, Duy Tan University, Da Nang, Vietnam
| | - Giang Thu Vu
- Center of Excellence in Evidence-based Medicine, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam
| | - Hai Quang Pham
- Institute for Global Health Innovations, Duy Tan University, Da Nang, Vietnam
| | - Hoa Thi Do
- Centre of Excellence in Artificial Intelligence in Medicine, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam
| | - Carl A Latkin
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Wilson Tam
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Cyrus S H Ho
- Department of Psychological Medicine, National University Hospital, Singapore, Singapore
| | - Roger C M Ho
- Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam.,Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Institute for Health Innovation and Technology, National University of Singapore, Singapore, Singapore
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Harrer S, Shah P, Antony B, Hu J. Artificial Intelligence for Clinical Trial Design. Trends Pharmacol Sci 2019; 40:577-591. [PMID: 31326235 DOI: 10.1016/j.tips.2019.05.005] [Citation(s) in RCA: 171] [Impact Index Per Article: 34.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 05/28/2019] [Accepted: 05/28/2019] [Indexed: 12/23/2022]
Abstract
Clinical trials consume the latter half of the 10 to 15 year, 1.5-2.0 billion USD, development cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the investment into the trial itself but also the preclinical development costs, rendering the loss per failed clinical trial at 800 million to 1.4 billion USD. Suboptimal patient cohort selection and recruiting techniques, paired with the inability to monitor patients effectively during trials, are two of the main causes for high trial failure rates: only one of 10 compounds entering a clinical trial reaches the market. We explain how recent advances in artificial intelligence (AI) can be used to reshape key steps of clinical trial design towards increasing trial success rates.
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Affiliation(s)
- Stefan Harrer
- IBM Research, IBM Research Australia Lab, 3006 Melbourne, VIC, Australia.
| | - Pratik Shah
- Massachusetts Institute of Technology, Media Lab, 02139 Cambridge, MA, USA
| | - Bhavna Antony
- IBM Research, IBM Research Australia Lab, 3006 Melbourne, VIC, Australia
| | - Jianying Hu
- IBM Research, IBM T.J. Watson Research Center, 10598 Yorktown Heights, NY, USA
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