Evidence map›Paper›PMID 38706736›Full record

ArticleEJIFCC2024

Artificial Intelligence - Perception of Clinical Laboratories' Technical Staff a Nationwide Multicentre Survey in Pakistan.

Sibtain Ahmed, Aqueel Kapadia, Imran Ahmed Siddiqui, Asma Shaukat, Mohammad Dilawar Khan, Muhammad Qaiser Alam Khan, Sahar Iqbal, Ghazanfar Abbas, Adnan Mustafa Zubairi, Syed Haider Nawaz Naqvi and 3 more

Abstract read
In one paragraph

Article in EJIFCC, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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

13 authors.

Sibtain AhmedSection of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan.
Aqueel KapadiaSection of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan.
Imran Ahmed SiddiquiDepartment of Pathology, Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan.
Asma ShaukatDepartment of Chemical Pathology, Quaid-e-Azam Medical College, Bahawalpur, Pakistan.
Mohammad Dilawar KhanDepartments of Chemical Pathology, Chughtai Healthcare, Lahore, Pakistan.
Muhammad Qaiser Alam KhanDepartment of Chemical Pathology and Endocrinology, Armed Forces Institute of Pathology, Rawalpindi, Pakistan.
Sahar IqbalDepartment of Pathology, Dow International Medical College, Dow University of Health Sciences, Karachi, Pakistan.
Ghazanfar AbbasDepartment of Chemical Pathology, Shifa International Hospital, Islamabad, Pakistan.
Adnan Mustafa ZubairiClinical Laboratories - Outreach: Indus Hospital & Health Network, Karachi, Pakistan.
Syed Haider Nawaz NaqviDepartment of Chemical Pathology, Sindh Institute of Urology and Transplantation, Karachi, Pakistan.
Ayesha SadiqaSection of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan.
Lena JafriSection of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan.
Imran SiddiquiSection of Chemical Pathology, Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: As Artificial Intelligence (AI) technology continues to assimilate into various industries, there is a huge scope in the healthcare industry specifically in clinical laboratories. The perspective of the laboratory professionals can give valuable insight on the ideal path to take for AI implementation. Methods: The study utilized a cross-sectional survey design and was conducted at the section of Chemical Pathology, Department of Pathology and Laboratory Medicine, the Aga Khan University (AKU), Karachi, Pakistan in collaboration with Consultant Pathologists of 9 clinical laboratories associated with teaching hospitals across Pakistan from October-November 2023. The survey was for a duration of 2 weeks and was circulated to all working laboratory technical staff after informed consent. Results: A total of 351 responses were received, of which 342 (male=146, female=196) responses were recorded after exclusion. Respondents ranged from technologists, faculty, residents, and coordinators, and were from different sections (chemical pathology, microbiology, haematology, histopathology, POCT). Out of the total 312 (91.2%) of respondents stated that they were at least somewhat familiar with AI technology. Experts in AI were only 2.0% (n=7) of all respondents, but 90% (n=6) of these were < 30 years old. 76.3% (n=261) of the respondents felt the need to implement more AI technology in the laboratories, with time saving (26.1%) and improving performances of tests (17.7%) cited to be the greatest benefits of AI. Security concerns (n=144) and a fear of decreasing personal touch (n=143) were the main concerns of the respondents while the younger employees had an increased fear of losing their jobs. 76.3% were in favour of an increase in AI usage in the laboratories. Conclusion: This study highlights a favourable perspective among laboratory professionals, acknowledging the potential of AI to enhance both the efficiency and quality of laboratory practices. However, it underscores the importance of addressing their concerns in the thoughtful implementation of this emerging technology.

Indexed as

AI toolsArtificial intelligenceClinical laboratoryPakistanPathologySurvey

Identifiers

PMID38706736
PMCPMC11063789

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Registered trials

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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.