The Role of Artificial Intelligence in Antimicrobial Susceptibility Testing: Perceptions and Expectations of Medical Doctors of South Punjab, Pakistan.

Main Article Content

Iqra Saleem
Unaza Usmani
Sadia Kanwal
Ali Abuzar Raza
Sajida Zafar
Mehvish Javeed

Abstract

Objective: To evaluate clinicians’ reliance on antimicrobial susceptibility testing (AST) and their perceptions, trust, and readiness to adopt artificial intelligence (AI)–enabled AST in clinical practice.
Methodology: A descriptive cross‑sectional study was conducted among 366 healthcare workers in South Punjab, Pa-kistan. Data on reliance, perceptions of AI‑enabled AST, trust, implementation, and concerns were analyzed using chi‑square (χ ²) tests.
Results: Demographic analysis showed that 51.0% of participants had less than five years of clinical experience, with medical officers comprising 48.9% (P<0.001). Females predominated (59.2% vs. 40.7%; P<0.01). Most respondents practiced in Lahore (37.7%) or Multan (33.0%; P<0.001), with surgery (34.4%) and pediatrics (22.9%) being the leading departments (P<0.001). Reliance on AST was high: 69.6% strongly agreed and 21.5% agreed that AST guided prescrib-ing decisions, though 72.9% noted delays limited optimal selection. Interpretation was seen as judgment‑dependent (41.5% strongly agree). Perceptions of AI were highly positive: 84.4% strongly agreed AI could improve accuracy, 76.2% reduce inappropriate use, and 82.2% aid earlier decisions. Trust and willingness to adopt were strong (95.9% trusted validated AI; 93.4% were willing to use it alongside conventional AST). Concerns included transparency (35.2%), bias (59.8%), unclear legal responsibility (78.9%), and infrastructure (75.9%). Most supported training (81.1%) and regu-latory oversight (71.8%). Significant variation was observed across responses (P<0.001).
Conclusion: Clinicians view AI‑enabled AST as a promising complementary tool, contingent on validation, governance, training, and integration.
Keywords: Antimicrobial susceptibility testing; Artificial intelligence; Antimicrobial resistance; Clinical decision support; Stewardship; Healthcare workers.

Downloads

Download data is not yet available.

Article Details

How to Cite
Iqra Saleem, Unaza Usmani, Sadia Kanwal, Ali Abuzar Raza, Sajida Zafar, & Mehvish Javeed. (2026). The Role of Artificial Intelligence in Antimicrobial Susceptibility Testing: Perceptions and Expectations of Medical Doctors of South Punjab, Pakistan. JMMC, 17(1), 141-145. https://doi.org/10.62118/jmmc.v17i1.902
Section
Original Article