Mentioned in 82 AI use cases across 6 industries
AI/analytics combines TV, streaming, and online video results into one view so marketers can see which ads work best and spend money smarter.
Use AI to flag missing, inconsistent, or hard-to-standardize real-world data before it is used in evidence generation for submissions.
It helps trial teams pick sites that have historically enrolled diverse patient groups, so studies better reflect real-world populations.
The platform connects trial management with patient enrollment data and site payments so teams can manage study progress and pay sites on time from one connected workflow.
Citeline uses online tools and recruitment partners to help the right patients find trial information, learn about studies, and enroll more easily.
Once a trial is running, the system keeps checking whether enrollment is on track and warns teams early if recruitment is slowing down.
Helps trial teams see how many people are joining a study, how fast enrollment is happening, and whether they are on track versus plan.
It helps study teams spot patients in a hospital system who may fit a trial, so they can focus outreach on the right people faster.
An AI system reads patient chart information and quickly flags people who might qualify for oncology trials, instead of relying only on manual staff review.
A built-in tool helps teams find important medical experts and opinion leaders relevant to their trial planning work.
Use AI tools to check whether outside patient data is good enough and similar enough to fairly compare against a new drug study.
An AI system reviews adaptive clinical trial plans and results to flag places where the study design or interpretation may make the final efficacy conclusion less reliable.
It estimates where and with whom a trial is most likely to enroll patients successfully, so teams can choose better locations and partners.
Managers get live dashboards showing where every submission document stands, plus outside regulatory intelligence that helps them make better filing decisions.
Genentech and Flatiron Health use real patient-care data to design cancer trials that better match how treatment happens in everyday clinics, helping studies run faster and more efficiently.
Use AI to find a fair comparison group outside a clinical trial so researchers can judge whether a new drug helped patients.
AI watches many places where safety issues might appear after a drug is sold and flags possible cases for the safety team to review.
FDA used the safety-review workflow to decide whether Entresto's pediatric warning label needed changes, and the reviewed data did not show a new problem.
An AI reviewer checks whether an RWE proposal clearly explains fit-for-use data, study design quality, and regulatory conduct so the sponsor can improve the package before submission.
AI looks at questions from agencies like the FDA and helps teams figure out why reviewers asked them, so the next submission can avoid the same problems.
AI agents read trial rules, search patient records safely, find eligible participants, and combine incoming trial data so teams can make decisions faster.
An AI system reads the FDA’s Q13 guidance and pulls out the important regulatory facts teams need when designing or documenting continuous manufacturing processes.
An ML system estimates how efficiently a planned clinical trial will recruit patients and how long it may take, based on the trial’s design choices.
Use AI to turn FDA decentralized trial guidance into a checklist that maps each study activity to the right setting, such as telehealth, home visit, local provider, or traditional site.
Use AI to test different trial designs on a computer first, so sponsors can see whether the study will include enough different kinds of patients to understand how the drug works for them.