Evidence map›Paper›PMID 41996369›Full record

ArticlePloS one2026

Evidence-based action plan for integrating artificial intelligence in an academic medical centre-a multidisciplinary approach.

Lena Jafri, Hafsa Majid, Aysha Habib Khan, Aqueel Kapadia, Shanzay Rehman, Kiran Hilal, Imran Siddiqui, Arsala Farooqui, Kulsoom Ghias, Naveed Rashid and 9 more

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

19 authors.

Lena JafriSection of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan.ORCID https://orcid.org/0000-0001-6807-5429
Hafsa MajidSection of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan.ORCID https://orcid.org/0000-0001-7506-7988
Aysha Habib KhanSection of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan.
Aqueel KapadiaSection of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan.
Shanzay RehmanSection of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan.
Kiran HilalDepartment of Radiology, AKU, Pakistan.
Imran SiddiquiSection of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan.
Arsala FarooquiInstitute for Global Health and Development, AKU.
Kulsoom GhiasDepartment of Biological & Biomedical Sciences, AKU, Pakistan.
Naveed RashidDepartment of Medicine, AKU, Pakistan.
Yousra SarfarazSection of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan.
Muhammad Umer Naeem EffendiSection of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan.
Syed Murtaza Raza KazmiDepartment of Surgery, AKU, Pakistan.
Muneeb KhalidAga Khan Medical College, Pakistan.
Khairulnissa AjaniSchool of Nursing & Midwifery, AKU, Pakistan.
Qamar RiazDepartment of Post Graduate Medical Education, AKU, Pakistan.
Mehmood RiazDepartment of Medicine, AKU, Pakistan.
Rozina RoshanQuality and Patient Safety Department and Department of Infection Prevention & Hospital Epidemiology, AKU, Pakistan.
Sibtain AhmedSection of Chemical Pathology, Department of Pathology and Laboratory Medicine Aga Khan University (AKU), Karachi Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAs the effectiveness of artificial intelligence (AI) in enhancing various facets of healthcare delivery becomes more apparent, it is anticipated that AI will soon find its way into standard clinical practices, even in low and middle-income countries. The objective of this study was to create an action plan for integrating AI into medical education, research, and clinical practices utilizing the SWITCH model of change integrating both rational and emotional aspects.

methodsThis exploratory qualitative study employed reflexive thematic analysis of semi-structured interviews and a co-design workshop, followed by the collaborative development of an action plan. The study was conducted from May 2023 to May 2024, at the Aga Khan University, Karachi, Pakistan. The development of an action plan was informed by interviews, co-design workshop, and discussions with diverse group of academic leaders, healthcare professionals and medical students. All interviews, workshop sessions, and planning meetings were audio recorded, transcribed verbatim, and anonymized. Data management was conducted manually using Microsoft Word and Excel. Findings after thematic analysis of the qualitative interviews and findings from the co-design workshop were gradually, and inductively transformed into the content for the action plan for integrating AI into healthcare.

resultsThe content analysis of interviews identified following four themes: AI opportunities, apprehensions toward AI-induced changes, pushing change through leadership styles, and importance of AI related capacity building. During the workshop, participants discussed aligning current AI knowledge with future requirements by identifying clear instructions, emotional motivators, and environmental changes required on the path of AI integration. The proposed action plan conceptualized AI integration as multidimensional change process comprising three domains: strategic actions, change pathway enablement and environmental readiness and human motivation operationalized through twenty actionable components.

conclusionThe study findings provide a context specific conceptual action plan for healthcare professionals to integrate AI into medical education, clinical service and research, Future work should focus on pilot implementation and empirical validations of the action plan across diverse healthcare settings to assess feasibility, effectiveness and scalability.

Indexed as

Academic Medical CentersArtificial IntelligenceDelivery of Health CareEducation, MedicalHumansPakistanQualitative Research

Identifiers

PMID41996369
PMCPMC13089692

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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.