Evidence map›Paper›PMID 39776314›Full record

ReviewThe AAPS journal2025

Innovative Approaches in Regulatory Affairs: Leveraging Artificial Intelligence and Machine Learning for Efficient Compliance and Decision-Making.

C S Ajmal, Sravani Yerram, V Abishek, V P Muhammed Nizam, Gayatri Aglave, Jayasri Devi Patnam, Rajeev Singh Raghuvanshi, Saurabh Srivastava

Abstract readReview
PubMed Publisher
In one paragraph

Review in The AAPS journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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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

8 authors.

C S AjmalDepartment of Regulatory Affairs, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India.
Sravani YerramDepartment of Regulatory Affairs, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India.
V AbishekDepartment of Regulatory Affairs, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India.
V P Muhammed NizamDepartment of Regulatory Affairs, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India.
Gayatri AglaveDepartment of Regulatory Affairs, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India.
Jayasri Devi PatnamDepartment of Regulatory Affairs, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India.
Rajeev Singh RaghuvanshiCentral Drugs Standard Control Organization (CDSCO), Directorate General of Health Services, Ministry of Health & Family Welfare, Government of India, New Delhi, India.
Saurabh SrivastavaDepartment of Regulatory Affairs, National Institute of Pharmaceutical Education and Research (NIPER), Hyderabad, Telangana, India. saurabh@niperhyd.ac.in.ORCID 0000-0003-0723-4363

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) and AI-driven technologies are transforming industries across the board, with the pharmaceutical sector emerging as a frontrunner beneficiary. This article explores the growing impact of AI and Machine Learning (ML) within pharmaceutical Regulatory Affairs, particularly in dossier preparation, compilation, documentation, submission, review, and regulatory compliance. By automating time-intensive tasks, these technologies streamline workflows, accelerate result generation, and shorten the product approval timeline. However, despite their immense potential, AI and ML also introduce new challenges. Issues such as AI software validation, data management security and privacy, potential biases, ethical concerns, and change management requirements must be addressed. This review highlights current AI-based tools actively used by regulatory professionals such as DocShifter, Veeva Vault, RiskWatch, Freyr SubmitPro, Litera Microsystems, cortical.io etc., examines both the benefits and obstacles of integrating these advanced systems into regulatory practices. Given the rapid pace of technological innovation, the article underscores the need for proactive collaboration with regulatory bodies to manage these developments. It also stresses the importance of adapting to evolving regulatory frameworks and embracing new technologies. Although regulatory agencies like the United Sates Food and Drug Administration (USFDA), European Medicines Agency (EMA), and Medicines and Healthcare products Regulatory Agency (MHRA) are working on guidelines for AI and ML adoption, clear, standardized protocols are still in the works. While the journey ahead may be complex, the integration of AI promises to fundamentally reshape regulatory processes and accelerate the approval of safe, effective pharmaceutical products.

Indexed as

Artificial IntelligenceDrug IndustryMachine LearningDecision Makingartificial intelligencedecision makingmachine learningregulatory complianceregulatory intelligence

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.