智能合规：用人工智能改变临床试验披露

Intelligent Compliance: T ransformingClinical T rial D isclosure w ith A I Executive summary The clinical trial disclosure landscape is evolvingrapidly, with increasing regulatory complexity,expanding disclosure requirements, and growingdemands for transparency. This presentssignificant challenges for sponsors, who mustnavigate a complex web of global regulationsand ensure trial information is disclosedaccurately, consistently, and efficiently. ensuring conformance to registry requirements,generating plain language summaries, andsafeguarding sensitive data. While AI presents significant potential, it’snecessary to approach its adoption responsibly.This involves addressing the associatedchallenges and mitigating risks related to dataprivacy, bias, and overreliance. Responsible AIadoption necessitates robust data protectionmeasures, careful model training, and ongoinghuman oversight. By embracing AI strategicallyand ethically, the clinical research communitycan harness its power to drive efficiency,compliance, and patient centricity in clinical trialdisclosure. Artificial intelligence (AI) offers a transformativesolution to these challenges. By automatingtasks, improving accuracy, and enhancingtransparency, AI can help sponsors streamlinetheir clinical trial disclosure processes andmeet the growing demands for timely andaccessible information. These tools can alsohelp with data extraction and organization, Intelligent Compliance: T ransformingClinical T rial D isclosure w ith A I The evolving landscape of clinical trialdisclosure The call for greater transparency in clinicaltrials has intensified in recent years, driven byheightened scrutiny from regulators, patients,investors, and the public. Stakeholders demandaccess to clinical research data with anemphasis on open science and collaborativeresearch. This shift towards transparency alignswith the broader societal movement towardspatient empowerment and informed decisionmaking in healthcare. Patients increasingly seekaccess to comprehensive and understandabletrial information, including plain languagesummaries of protocols and results. Meetingthese evolving expectations requires apatient centric approach to disclosure, whereinformation is clear, concise, and accessible. The clinical trial disclosure landscape isincreasingly complex, with a patchwork ofevolving regulations across different countriesand registries. Sponsors and investigators mustmanage a constant influx of new and updatedrequirements, making it challenging to stayabreast of the latest guidelines. For example,over 130 regulatory guidance documentsand laws related to clinical trial disclosurewere published in the past year alone, withapproximately 30% already superseded. Thisrapid pace of change necessitates continuousmonitoring and adaptation, placing a significantburden on organizations. Noncompliancewith these regulations can have seriousconsequences, including financial penalties,legal repercussions, loss of investors’ trust, anddamage to an organization’s reputation. AI technologies offer a promising solutionto some of these challenges. They have thepotential to streamline processes, enhancecompliance, and improve the quality andaccessibility of clinical trial information. Byautomating tasks, facilitating data analysis,and enabling intelligent decision making, AI canhelp organizations navigate the complexitiesof the disclosure landscape while meeting thegrowing demands for transparency and patientengagement. Moreover, the scope of disclosure itself isexpanding, encompassing a wider range ofclinical documents and data, including fullprotocols, clinical study reports (CSRs), andanonymized patient data. This broadeningscope adds another layer of complexity toan already challenging process. Navigatingthese requirements demands a proactive andstrategic approach to ensure compliance andavoid potential penalties. Intelligent Compliance: T ransformingClinical T rial D isclosure w ith A I Unlocking disclosure efficiencies with AI:key use cases AI-extracted information is accurate, complete,and fit for purpose in clinical trial disclosure. Intelligent data ingestionClinical trial disclosure involves extracting, organizing, and submitting significant amountsof data from disparate sources, includingprotocols, statistical analysis plans, clinicalstudy reports (CSRs), and various clinicalsystems. Traditionally, this task has been laborintensive and time consuming, prone to errorand inefficiencies. AI-powered intelligentdata ingestion can start to automate theseprocesses, significantly improving efficiency,accuracy, and compliance. Language translationAs clinical trials are increasingly conducted in multiple countries, requiring the disclosureof information in multiple languages, AI-powered translation is becoming more valuable.It enables reasonably accurate translationof complex clinical terms and patient-facing documents. This facilitates globaltrial registration and results disclosure