Chinese Journal of Medical History / Zhong Hua Yi Shi Za Zhi

[This article belongs to Volume - 54, Issue - 02]

Abstract : Despite existing studies investigating pharmacologists' and pathologists` perspectives towards AI in medicine, there is a noticeable research gap concerning the artificial intelligence-based decision-making drug discovery on a national scale. This national, cross-sectional study aims to evaluate the perspective of pharmacologists and pathologists in artificial intelligence-based decision-making drug discovery in Pakistan. We have accomplished cross-sectional multi-setting research from December 1, 2023, to March 1, 2024, at Frontier Medical and Dental College, Abbottabad, Pakistan; Ayub Medical College, Abbottabad, Pakistan; and Women Medical and Dental College, Abbottabad, Pakistan. The institute's ethics committee accorded the research project its approval. With SPSS Statistics v.26, we carried out the statistical analysis. The differences between agreement levels were calculated using the Mann-Whitney U test. Furthermore, the significance of frequencies related to the objective was assessed using the Chi-squared (χ2) test. The multi-logistic regression model was applied progressively. A total of 120 experts participated in a study among which 68 were pathologists and 52 pharmacologists. The median age of participating pharmacologists was 36 (30–44.6) years, and pathologists were 34 (32–49.5) years. Approximately 69.2% were male pharmacologists, and 61.7% were male pathologists. 99% of pathologists and 94.2% of pharmacologists agree with using AI in drug discovery, showing a significant value of <0.000. 40.3% of pharmacologists and 82.3% of pathologists agreed that they could speed up the diagnosis process than pharmacologists and pathologists, with a significant value of 0.001. It is concluded that Artificial intelligence (AI) is increasingly being used in drug discovery, but pharmacologists and pathologists are often unaware of its potential applications. AI algorithms can help tailor medicine approaches, improve treatment outcomes, and enhance patient adherence.