Pharmacovigilance is the science of monitoring and assessing the safety, efficacy, and quality of drugs through pre-marketing clinical trials and post-marketing surveillance. Accessed May 19, 2022, [11] https://www.iqvia.com/-/media/iqvia/pdfs/library/white-papers/ai-in-clinical-development.pdf With the AIA the EC introduced a first attempt to regulate the application of AI on cross-sectoral level to ensure compliance with fundamental rights. PMC Federal government websites often end in .gov or .mil. Before joining Deloitte she was a Principal Investigator at the Italian Institute of Health and lead internationally recognised research on neurodegenerative diseases, specifically on novel diagnostic and therapeutic approaches, filing a relevant patent in the field. [6] https://www2.deloitte.com/content/dam/insights/us/articles/22934_intelligent-clinical-trials/DI_Intelligent-clinical-trials.pdf Therefore, AI-enabled technologies nowadays provide support in generating evidence to avoid redundancies at this stage. Once the stuff of science fiction, AI has made the leap to practical reality. Prashant Tandale. Accessed May 19, 2022, [7] https://www.globaldata.com/ She supports the Healthcare and Life Sciences practice by driving independent and objective business research and analysis into key industry challenges and associated solutions; generating evidence based insights and points of view on issues from pharmaceuticals and technology innovation to healthcare management and reform. already exists in Saved items. This OPED is chilling on what can happen as the lipid nanoparticles distribute to the brain. Francesca is a Research Manager for the Deloitte UK Centre for Health Solutions. Accessed May 19, 2022, [8] https://www.antidote.me It remains to be seen how this will impact the use and development of AI-enabled technologies in the field of clinical research. . doi: 10.1002/ams2.740. This presentation will discuss how to implement AI in the workflow and discuss three examples where organizations have successfully done this. 2022 May 25;23(11):5938. doi: 10.3390/ijms23115938. On the 20 th of May Paolo Morelli, CEO of Arithmos, joined the Scientific Board of Italian ePharma Day 2020 to discuss the growing role of the new technologies in clinical trials. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. The AIA addresses all sectors and does not specifically mention the area of clinical development. However, they have often lacked the skills and technologies to enable them to utilise this data effectively. Knowledge graphs and graph convolutional network applications in pharma. [14] https://artificialintelligenceact.eu/the-act/ Another example is the platform Antidote that uses machine learning to match patients as potential participants with clinical trials (8). There are different types of Artificial Intelligence in different sectors, such as Health, Manufacturing, Infrastructure, Business and others. Patient monitoring, medication adherence and retention: AI algorithms can help monitor and manage patients by automating data capture, digitalising standard clinical assessments and sharing data across systems. Machine Learning (ML) is a type of AI that is not explicitly programmed to perform . In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Pharmacovigilance is the science of monitoring and assessing the safety, efficacy, and quality of drugs through pre-marketing clinical trials and post-marketing surveillance. FOIA Email a customized link that shows your highlighted text. View in article, Aditya Kudumala, Leverage operational data with clinical trial analytics:Take three minutes to learn how analytics can help, Deloitte Development LLC, accessed December 18, 2019. Artificial Intelligence (AI) supported technologies play a crucial role in clinical research: For example, during the COVID-19 pandemic the Biotech Company BenevolentAI found through a machine-learning approach that the kinase inhibitor Baricitinib, commonly used to treat arthritis, could also improve COVID-19 outcomes. [4] https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:32001L0083:EN:HTML Simply select text and choose how to share it: Intelligent clinical trials Oculomics uses the convergence of multimodal imaging techniques and large-scale data sets to characterize macroscopic, microscopic, and molecular ophthalmic features associated with health and disease (13). Regulatory agencies such as the FDA (Food and Drug Administration) play an important role in ensuring that drugs meet certain standards regarding safety and efficacy before they enter the market. Usually it may take up to 12 years from discovery to marketing with involved costs of up to 2.6 billion US-Dollars. AI/ML is over-hyped, this panel will discuss machine learning techniques that are in production in various organizations that are adding value and accelerating Clinical Development. AI algorithms, in combination with wearable technology, can enable continuous patient monitoring and real-time insights into the safety and effectiveness of treatment while predicting the risk of dropouts, thereby enhancing engagement and retention.6, 5. For example, the mentioned drug repurposing of Baricitinib to treat COVID-19 patients, discovered by AI-tools, allowed for building on existing evidence. The pharmaceutical company Roche already applied such an AI-driven model in a Phase II study (9). Bookshelf and transmitted securely. Medical and operational experts can incorporate AI algorithms into use cases including automation of image analysis, predictive analytics about trends in the meta data, and tailored patient engagement for improved compliance. Read the full report, Intelligent clinical trials: Transforming through AI-enabled engagement, for more insights. Keywords: While several interest groups commented publicly on the AIA and provided extensive position papers (e.g. Med. Using principles of fairness in machine learning, a model that maps clinical trial descriptions to a ranked list of sites was developed and tested on real-world data. 2021 May;268(5):1623-1642. doi: 10.1007/s00415-019-09518-3. To deal with the circumstance in which one disease influences the clinical presentation of another, the program must also have the capacity to reason from cause to effect. 2020;9:7177. If biopharma succeeds in capitalising on AIs potential, the productivity challenges driving the decline in. doi: 10.15420/aer.2019.19. Join the ranks of a highly successful industry and reap its rewards! Neal Grabowski, Director, Safety Data Science, AbbVie, Inc. Nekzad Shroff, Vice President, Product Management, Saama Technologies, Aditya Gadiko, Director of Clinical Informatics, Saama Technologies, Nicole Stansbury, Vice President, Clinical Monitoring, Central Monitoring Services, Syneos Health, Pre-Con User Group Meetings & Hosted Workshops, Kick-Off Plenary Keynote and 6th Annual Participant Engagement Awards, Protocol Development, Feasibility, and Global Site Selection, Improving Study Start-up and Performance in Multi-Center and Decentralized Trials, Enrollment Planning and Patient Recruitment, Patient Engagement and Retention through Communities and Technology, Resource Management and Capacity Planning for Clinical Trials, Relationship and Alliance Management in Outsourced Clinical Trials, Data Technology for End-to-End Clinical Supply Management, Clinical Supply Management to Align Process, Products and Patients, Artificial Intelligence in Clinical Research, Decentralized Trials and Clinical Innovation, Sensors, Wearables and Digital Biomarkers in Clinical Trials, Leveraging Real World Data for Clinical and Observational Research, Biospecimen Operations and Vendor Partnerships, Medical Device Clinical Trial Design, and Operations, Device Trial Regulations, Quality and Data Management, Building New Clinical Programs, Teams, and Ops in Small Biopharma, Barnett Internationals Clinical Research Training Forum, SCOPE Venture, Innovation, & Partnering Conference, Clinical Trial Forecasting, Budgeting and Contracting. AI-enabled technologies might make specifically the usually cost-intensive Orphan Drug development more economically viable. Where are their voices being heard and what can we learn from the cultural experiences they weave into their research methodologies and daily practices? Its main objective is to detect adverse effects that may arise from using various pharmaceutical products. Virtual trials enable faster enrolment of more representative groups in real-time and in their normal environment and monitoring of these patients remotely. This means that high-risk AI systems (amongst others defined as systems that pose significant risks to the health and safety or fundamental rights of persons and systems that can lead to biased results and entail discriminatory results, ibid. Increasing amounts of scientific and research data, such as current and past clinical trials, patient support programmes and post-market surveillance, have energised trial design. Accessed May 19, 2022, [2] https://www.exscientia.ai/ Understand key learnings from early adopters of AI-based technologies within the ICSR process. Applications of Machine Learning in Cardiac Electrophysiology. The drug candidate moved into trial phase in late 2021. Many college and school students are asked to bring presentations on Artificial Intelligence especially class 10 and 12 board students. The Directive on the Community code relating to medicinal products for human use (Directive 2001/83/EC, Annex I, Part 3, II A.1) foresees that in vivo experiments mustnt be replaced (4). Artificial intelligence can reduce clinical trial cycle times while improving the costs of productivity and outcomes of clinical development. Hence if you are looking for PPT and PDF on AI, then you are at the right place. The global Contract Research Organization IQVIA states that using machine-learning tools globally increased enrolment rates by 20.6 % in the field of oncology compared to traditional approaches (11). The Deloitte Centre for Health Solutions (CfHS) is the research arm of Deloittes Life Sciences and Health Care practices. How do new techniques like transformers help with better language models? Pduraru DN, Niculescu AG, Bolocan A, Andronic O, Grumezescu AM, Brl R. Pharmaceutics. However, on cross-sectoral level the European Commission (EC) published within the Artificial Intelligence Act (AIA) a proposal of harmonized rules on Artificial Intelligence. Artificial Intelligence (AI) is a broad concept of training machines to think and behave like humans. This report is the third in our series on the impact of AI on the biopharma value chain. Recent Advances in Managing Spinal Intervertebral Discs Degeneration. Accessed May 19, 2022, Read about ideas & tools for effective clinical research, Follow todays topics in clinical research, Knowledge base: study design, study management, digitalization & data management,biostatistics, safety, I have read and accept the Privacy Policy, Visit here our corporate page to find out more about our CRO services, Business Development Management @GKM Gesellschaft fr Therapieforschung mbH. Artificial intelligence in medical Imaging: An analysis of innovative technique and its future promise. EDISON, N.J., Jan. 10, 2023 (GLOBE NEWSWIRE) -- Hepion Pharmaceuticals, Inc. (NASDAQ:HEPA), a clinical stage biopharmaceutical company focused on Artificial Intelligence ("AI")-driven . 1. Artificial intelligence has the potential to revolutionize modern society in all its aspects. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. The PowerPoint PPT presentation: "Welcoming AI in the Clinical Research Industry" is the property of its rightful owner. These partnerships combine tech giants and startups core expertise in digital science with biopharmas knowledge and skills in medical science.10. Artificial intelligence for predicting patient outcomes Healthcare data is intricate and multi-modal . You might even have a presentation youd like to share with others. government site. Traditional linear and sequential clinical trials remain the accepted way to ensure the efficacy and safety of new medicines. Pharmacovigilance is the study of two primary outcomes in the pharmaceutical industry: safety and efficacy. What is the perspective of Black professionals and patient advocates as the medical and scientific industries grapple with effective ways to engage minority population? Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is practised. Methods A total of 168 patients from three centers were divided into training, validation, and test groups. The main challenges in AI clinical integration. Now they are starting to make their way into the clinical research realm advancing clinical operations, as well as data management. PowerPoint-Prsentation Author: Microsoft Office-Anwender Keywords: Optimiert fr PowerPoint 2010 PC Created Date: 11/28/2019 12:22:11 PM . View in article, Angie Sullivan, Clinical Trial Site Selection: Best Practices, RCRI Inc, accessed December 18, 2019. Getting Started in Pharmacovigilance Part 1, Coberts Manual of Pharmacovigilance and Drug Safety, Investigational product (IP): Any drug, device, therapy, or intervention after Phase I trial, Event: Any undesirable outcome (i.e. The .gov means its official. After feedback iterations throughout the past years, the AIA is currently under review at the European Parliament. Incorporating a self-learning system, designed to improve predictions and prescriptions over time, together with data visualisation tools can proactively deliver reliable analytics insights to users.7, 6. As many as half of all trials could be done virtually, with convenience improving patient retention and accelerating clinical development timelines.13. Artificial Intelligence (AI) for Clinical Trial Design. The risk of lacking consistency and standards in terms of regulatory approaches; The insufficient protection of the environment; The need to address not only users but also end recipients (15). Examples of AI potential applications in clinical care. The healthcare industry, being one of the most sensitive and responsible industries, can make . Two recent programs, for example, combine the scoring methods of Internist . 1, Clinical prediction models in the COVID-19 pandemic, Move Closer to your Patients in order to Improve Recruitment, Digitalisierung im Gesundheitswesen, Teil 2, Visit here our corporate page to find out more about our, GKM Gesellschaft fr Therapieforschung mbH. Outsourcing and strategic relationships to obtain necessary AI skills and talent: Biopharma companies are looking to strategic and operational relationships based on outsourcing and partnership models. You will be able to open up a world of opportunities in pharmacovigilance and get qualified for entry-level roles as drug safety jobs: Common titles for pharmacovigilance officer jobs include: Drug Safety Officer, Pharmacovigilance Officer, PV Officer, Drug Safety Quality Assurance Officer, Clinical Safety Manager, Global Regulatory Affairs & Safety Strategic Lead, Medical Safety Physician/MD/MBBS or IMG, Risk Management and Mitigation Specialist, Clinical Scientist Advisor in Pharmacovigilance and Drug Surveillance, Drug Regulatory Affairs Professional with PV Knowledge and Experience, Senior Regulatory Affairs Associate with PV Expertise and Knowledge, Senior Clinical Trial Safety Associate or Specialist, MedDRA Coder (Medical Dictionary for Regulatory Activities), PV Compliance Reviewer or Auditor, GCP (Good Clinical Practices) Specialist with PV Knowledge and experience. The drug received authorization for emergency use by the FDA in 2021 (1). View in article, U.S. Food and Drug Administration (FDA), Submitting Documents Using Real-World Data and Real-World Evidence to FDA for Drugs and Biologics Guidance for Industry, May 2019, accessed December 18, 2019. Newell Hall, Room 202. An official website of the United States government. Artificial Intelligence has the potential to dramatically improve the speed and accuracy of clinical trials. Drug candidates that prove to be ineffective or toxic to organoids may not require further testing in animal experiments. Articles 30, 43). It has millions of presentations already uploaded and available with 1,000s more being uploaded by its users every day. Regulators around the globe have released guidance to encourage biopharma companies to use RWD strategies.11 Innovative trials using RWD are likely to play an increasing role in the regulatory process by defining new, patient-centred endpoints. 2022 Jun 9;14(12):2860. doi: 10.3390/cancers14122860. Translational vision science & technology 9(2), 6-6. The applications of AI could lead to faster, safer and significantly less expensive clinical trials. Furthermore, such technologies may automate manual processing tasks (e.g. The German Federal Ministry of Food and Agriculture awarded two scientists with the 2021 Animal Welfare Research Prize for developing an automated manufacturing process of midbrain organoids. While AI is yet to be widely adopted and applied to clinical trials, it has the potential to transform clinical development. Artificial Intelligence (AI) supported technologies play a crucial role in clinical research: For example, during the COVID-19 pandemic the Biotech Company BenevolentAI found through a machine-learning approach that the kinase inhibitor Baricitinib, commonly used to treat arthritis, could also improve COVID-19 outcomes. The course is accredited and designed to help those who want to move into clinical research or enhance their profile in their existing company. E: chi@healthtech.com, Micah Lieberman, Executive Director, Cambridge Healthtech Institute (CHI), Meghan McKenzie, Principal, Inclusion, Patient Insights and Health Equity, Chief Diversity Office, Genentech, Kimberly Richardson, Research Advocate, Founder, Black Cancer Collaborative, Karriem Watson, PhD, Chief Engagement Officer, NIH. Available online 17 January 2023, 102491. 2022 Mar 1;9(1):e740. And, again, its all free. Clipboard, Search History, and several other advanced features are temporarily unavailable. Reproduced from [14], Elsevier B.V. 2021. Medical Applications of Artificial Intelligence (Legal Aspects and Future Prospects) Laws. It includes ingestion of data from many sources, aggregation via programming, cleaning through listings review and validation checks, and provisioning of data to downstream stakeholders in various formats. In addition, suboptimal patient selection, recruitment and retention, together with difficulties managing and monitoring patients effectively, are contributing to high trial failure rates and raising the costs of research and development.2. Well convert it to an HTML5 slideshow that includes all the media types youve already added: audio, video, music, pictures, animations and transition effects. Monique Phillips, Global Diversity and Inclusion Lead, Bristol Myers Squibb Co. Nikhil Wagle, MD, Assistant Professor, Harvard Medical School, Dana-Farber Cancer Institute, Timothy Riely, Vice President, Clinical Data Analytics, IQVIA. The site is secure. For example, Insilico Medicine states that the process of discovering and moving its candidate into trial phase cost 2.6 million US-Dollars, significantly less than it had cost without using AI-enabled technologies (12). Cancers (Basel). Faisal Khan, PhD, Executive Director, Advanced Analytics & AI, AstraZeneca Pharmaceuticals, Inc. However, in most diseases, disease-relevant markers are spread across multiple biological contexts that are observed independently with different measurement technologies and at various time schedules, and their manual interpretation is therefore in many cases complex. Why is it both a moral and a business imperative? Artificial intelligence (AI) and machine learning (ML) have propelled many industries toward a new, highly functional and powerful state. However, the lengthy tried and tested process of discrete and fixed phases of randomised controlled trials (RCTs) was designed principally for testing mass-market drugs and has changed little in recent decades (figure 1).1, Download the complete PDF and get access to six case studies, Read the first and second articles of the AI in Biopharma collection, Explore the AI & cognitive technologies collection, Learn about Deloitte's Life Sciences services, Go straight to smart. artificial intelligence; clinical applications; deep learning; machine learning; personalized medicine; precision medicine. doi: 10.1016/j.matpr.2021.11.558. monitor conversations on social media and other platforms) (10). [13] Wagner, S. K., Fu, D. J., Faes, L., Liu, X., Huemer, J., Khalid, H., & Keane, P. A. 2022 May 25;23(11):5954. doi: 10.3390/ijms23115954. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., & Aspuru-Guzik, A. Karen also produces a weekly blog on topical issues facing the healthcare and life science industries. Medical and operational experts can incorporate AI algorithms into use cases including automation of image analysis, predictive analytics about trends in the meta data, and tailored patient engagement for improved compliance. Brian Martin, Head of AI, R&D Information Research, Research Fellow, AbbVie, Inc. Malaikannan Sankarasubbu, Vice President, Artificial Intelligence Research, Saama Technologies, Inc. Jason Attanucci, Vice President and General Manager, Life Sciences, Deep 6 AI, Lucas Glass, Vice President,Analytics Center of Excellence, R&D Solutions, IQVIA, ukasz Kidziski, PhD, Director, AI, Clario, Janine Jones, Senior Product Manager, Clario, David Billiter, Founder and CEO, Deep Lens, Patrick Schwab, PhD, Director, Artificial Intelligence and Machine Learning, GSK. AI for Clinical Data Utilization Across Full Product Cycle. 2021;4:5461. The use of artificial intelligence, machine learning and deep learning in oncologic histopathology. Careers. If so, just upload it to PowerShow.com. Neurotransmitters-Key Factors in Neurological and Neurodegenerative Disorders of the Central Nervous System. View in article, Stefan Harrer et al., Artificial Intelligence for Clinical Trial Design, ScienceDirect, August 2019, accessed December 18, 2019. 2020 Oct;49(9):849-856. doi: 10.1111/jop.13042. We have taken this opportunity to talk to him about one of the most debated technologies of the last few years . artificial intelligence in pharmacovigilance ppt. AI-enabled technologies, having unparalleled potential to collect, organise and analyse the increasing body of data generated by clinical trials, including failed ones, can extract meaningful patterns of information to help with design. Even additional research fields may emerge, as it is the case with Oculomics. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee ("DTTL"), its network of member firms, and their related entities. She holds a BSc and MSc in Biological Engineering from IST, Lisbon. Do you have PowerPoint slides to share? (2019). However, the possible association between AI . Todays medical monitors are under tremendous pressure to quickly identify trends and signals that could impact patient safety and drug efficacy. Next to disciplines like sciences, information technologies and law, other expertise will gain importance like ethics and social sciences. We combine creative thinking, robust research and our industry experience to develop evidence-based perspectives on some of the biggest and most challenging issues to help our clients to transform themselves and, importantly, benefit the patient. Combining Automated Organoid Workflows with Artificial IntelligenceBased Analyses: Opportunities to Build a New Generation of Interdisciplinary HighThroughput Screens for Parkinsons Disease and Beyond. View in article, Healthcare Weekly, Novartis uses AI to get insights from clinical trial data, March 2019, accessed December 18, 2019. AI algorithms, combined with an effective digital infrastructure, could enable the continuous stream of clinical trial data to be cleaned, aggregated, coded, stored and managed.3 In addition, improved electronic data capture (EDC) should can also reduce the impact of human error in data collection and facilitate seamless integration with other databases (figure 2). Our pharmacovigilance training and regulatory affairs certification is a course that takes one week to complete. We offer advanced courses with a combination of theory and practice-oriented learning, allowing students to acquire the experience necessary for this field. The role of AI in healthcare has been portrayed clearly and concisely. translate and digitize safety case processing documents) (11). 2021 Jun 10;14:17562848211017730. doi: 10.1177/17562848211017730. The potential of AI to improve the patient experience will also help deliver the ambition of biopharma to embed patient-centricity more fully across the whole R&D process. View in article, Dawn Anderson et al., Digital R&D: Transforming the future of clinical development, Deloitte Insights, February 2018, accessed December 17, 2019. Pharmacovigilance must happen throughout the entire life cycle of a drug, from when it is first being developed to long after it has been released on the market. Novel Research Applying Artificial Intelligence to Clinical Medicine 2.1. Artificial intelligence (AI)-enabled data collection and management can be a game changer for life sciences companies in the drug development process. Artificial Intelligence (AI) is a computer performing tasks commonly associated with human intelligence. A Review of Digital Health and Biotelemetry: Modern Approaches towards Personalized Medicine and Remote Health Assessment. The face of the world is changing and your success is tied to reaching ethnic minorities. Clinician (MBBS/MD) and Data Science specialist, with 18 years+ in the Health and Life Sciences industry, including over 12+ yrs in Advanced Analytics and Business Consulting and 6+ years into . CHIs 5th Annual Artificial Intelligence in Clinical Research conference is designed to facilitate the discussion and to accelerate the adoption of these approaches in clinical trials. Artificial Intelligence AI in Clinical Trials: Technology. Saxena S, Jena B, Gupta N, Das S, Sarmah D, Bhattacharya P, Nath T, Paul S, Fouda MM, Kalra M, Saba L, Pareek G, Suri JS. Dechallenge vs. Rechallenge: Causality assessed by measuring AE outcomes when withdrawing vs. re-administering IP, Causal relationship: Determined to be certain, probable/likely, or possible (AE + Causal -> ADR), Seriousness: based on outcome + guide to reporting obligations (i.e. Faculty Letter of Recommendation. 2021;56:22362239. Before joining Deloitte, Maria Joao was a postgraduate researcher in Bioengineering at Imperial College London, jointly working with Instituto Superior Tcnico, University of Lisbon. 2022 Jun 9;23(12):6460. doi: 10.3390/ijms23126460. 1. Operations consists of monitoring drug progress during preclinical trials as well researching real-world evidence regarding adverse effects reported by patients or healthcare professionals. Exceptional organizations are led by a purpose. Artificial intelligence methods, such as machine learning, can improve medical diagnostics. Post-marketing surveillance activities typically involve ongoing monitoring of drugs already available on the market in order to detect any unexpected adverse events or other issues that may not have been detected during pre-marketing tests. At the Centre she conducts rigorous analysis and research to generate insights that support the practice across Life Sciences and Healthcare. Leveraging AI and NLP technologies to mine, contextualize and temporalize medical concepts can have a dramatic effect on clinical trial operations. All new drugs must go through rigorous testing processes before they are approved for sale, which includes assessing any potential side effects or interactions with other medications. As with other industries, this is the beginning of an unknown road with respective regulations still in its very infancy. Articles 32-40) will have to comply with mandatory requirements for trustworthy AI and undergo a conformity assessment. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Therefore, specific implications in the field of clinical research may require an assessment on a case-by-case basis. 2. It become important to understand artificial intelligence, the types of artificial intelligence, and its application in day-to-day life. The development of novel pharmaceuticals and biologicals through clinical trials can take more than a decade and cost billions of dollars during that tenure period AI-enabled technologies may enhance operational efficiencies such as site and patient recruitment. -. Int J Mol Sci. It resulted in a list of potential trial-sites that accounted for performance and diversity. We will also discuss best practices, lessons learnt, how to pick a ML use case from idea to implementation and more. to receive more business insights, analysis, and perspectives from Deloitte Insights, Telecommunications, Media & Entertainment, Intelligent clinical trials: Transforming through AI-enabled engagement, Artificial Intelligence for Clinical Trial Design, Digital R&D: Transforming the future of clinical development, Clinical Trial Site Selection: Best Practices, The innovative startups improving clinical trial recruitment, enrollment, retention, and design, Leverage operational data with clinical trial analytics:Take three minutes to learn how analytics can help. Artificial Intelligence has the potential to dramatically improve the speed and accuracy of clinical trials. Learn which AI-based technologies are in production for which ICSR process steps.