Evidence map›Paper›PMID 40439837›Full record

ArticleTherapeutic innovation & regulatory science2025

The Adoption and Use of Artificial Intelligence and Machine Learning in Clinical Development.

Mary Jo Lamberti, Maria I Florez, Hana Do, Stephanie Rosner, Timothe Menard, Carrie Nielson, Amanda Donovan, Jingjing Ye, Sathish Kaveripakam, Birgit Schoeberl and 2 more

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Article in Therapeutic innovation & regulatory science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Mary Jo LambertiTufts Center for the Study of Drug Development, Tufts University School of Medicine, Boston, MA, USA. mary_jo.lamberti@tufts.edu.ORCID http://orcid.org/0000-0002-3720-7828
Maria I FlorezTufts Center for the Study of Drug Development, Tufts University School of Medicine, Boston, MA, USA.ORCID http://orcid.org/0009-0002-9327-8783
Hana DoTufts Center for the Study of Drug Development, Tufts University School of Medicine, Boston, MA, USA.
Stephanie RosnerDrug Information Association, Washington, DC, USA.
Timothe MenardF. Hoffmann-La Roche AG, Basel, CH-4070, Switzerland.ORCID http://orcid.org/0000-0003-4545-6944
Carrie NielsonGilead Sciences, Inc., Foster City, CA, USA.
Amanda DonovanTakeda Pharmaceutical CO Ltd., Tokyo, Japan.
Jingjing YeBeigene Ltd., Cambridge, MA, USA.
Sathish KaveripakamNovartis Pharma AG, Basel, CH-4056, Switzerland.
Birgit SchoeberlNovartis Pharma AG, Basel, CH-4056, Switzerland.
Alette R HuntNovartis Pharma AG, Basel, CH-4056, Switzerland.
Helen YeardleyICON Plc., Dublin, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of artificial intelligence (AI) and machine learning (ML) in drug discovery has been well documented, but measures of levels of adoption, investments, and efficiencies gained from its use in clinical development have not yet been developed, captured or published. AI/ML use in clinical development is expected to increase, but its impact has not yet been systematically measured until now.

methodsThe Tufts Center for the Study of Drug Development conducted a global online survey among pharmaceutical and biotechnology companies, contract research organizations (CROs), and data and technology vendors servicing drug developers. The survey gathered 302 responses assessing levels of AI/ML implementation across 36 distinct clinical trial planning and design, trial execution, and regulatory submission activities. The survey collected data on US dollar investment, time savings, and challenges and opportunities of AI/ML use in clinical development.

resultsApproximately one-third of the sample (36.9%) was not yet using or implementing AI/ML across 36 design and planning, execution, and regulatory submission activities; another 30.3% was beginning their AI/ML implementation (or piloting), 22.1% was partially implementing (or moving beyond pilots), and on average only 10.7% had fully implemented AI/ML (i.e., uses AI in most trials employing a repeatable process).

Indexed as

Artificial IntelligenceDrug DevelopmentDrug DiscoveryMachine LearningClinical Trials as TopicDrug IndustryHumansSurveys and QuestionnairesAdoption maturityArtificial intelligenceClinical developmentClinical trial designClinical trialsGenerative AIInnovation adoptionMachine learningProtocol complexityProtocol designQuality by designReal-world dataResearch and development

Identifiers

PMID40439837

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.