INFLUENCE OF ARTIFICIAL INTELLIGENCE ON CLINICAL DECISION-MAKING IN HEALTHCARE: A KAP STUDY AMONG FUTURE PHARMACISTS
Author(s)
GAUTAM SAHU, MS, PhD1, Sandeep Arora, MS, PhD2, Tapan Behl, MS, PhD2, Deepika Yadav, MS2.
1Assistant Professor, Amity Institute of Pharmacy, Amity University Punjab, Mohali, Punjab, India, Mohali, India, 2Pharmacy, Amity Institute of Pharmacy, Amity University Punjab, Mohali, Punjab, India, Mohali, India.
1Assistant Professor, Amity Institute of Pharmacy, Amity University Punjab, Mohali, Punjab, India, Mohali, India, 2Pharmacy, Amity Institute of Pharmacy, Amity University Punjab, Mohali, Punjab, India, Mohali, India.
OBJECTIVES: Artificial Intelligence (AI) is transforming healthcare delivery and clinical decision-making through early diagnosis, precision medicine, and patient-centred care. Future pharmacists are expected to play a crucial role in integrating AI into healthcare systems. This study assessed the knowledge, attitude, and practice (KAP) regarding AI in clinical decision-making among pharmacy students, emphasising healthcare affordability, accessibility, and patient outcomes.
METHODS: A cross-sectional questionnaire-based study was conducted among undergraduate pharmacy students using a structured and validated KAP survey administered through an online platform. The questionnaire included demographic details and 20 closed-ended questions related to AI applications in healthcare. Knowledge was assessed using multiple-choice questions, attitude through a 5-point Likert scale, and practice through adoption- and frequency-based responses. Scores were categorised into good, moderate, and poor levels using predefined cut-offs. Descriptive statistics, chi-square tests, and correlation analyses were performed.
RESULTS: A total of 176 students participated, including 118 females (67.0%) and 58 males (33.0%). Good knowledge regarding AI applications was observed in 109 participants (61.9%), while 126 participants (71.6%) demonstrated a positive attitude toward AI integration in clinical decision-making. However, only 87 participants (49.4%) reported adequate practical exposure to AI-based healthcare tools. Significant associations were observed between knowledge and attitude (χ²=8.14; p=0.004) and between knowledge and practice levels (χ²=11.27; p=0.001). A moderate positive correlation was identified between knowledge and practice scores (r=0.56; p<0.001). Approximately 119 participants (67.6%) believed AI could improve healthcare affordability and accessibility. Ethical concerns, data privacy issues, and lack of practical training were identified as major barriers to AI adoption.
CONCLUSIONS: The study demonstrates a knowledge-practice gap among future pharmacists regarding AI utilisation in healthcare. Although awareness and attitudes toward AI were favourable, limited practical exposure and concerns regarding ethics and data security hindered adoption. Integrating AI-focused education and hands-on training into pharmacy curricula may improve readiness for technology-driven, patient-centred, and cost-effective healthcare delivery.
METHODS: A cross-sectional questionnaire-based study was conducted among undergraduate pharmacy students using a structured and validated KAP survey administered through an online platform. The questionnaire included demographic details and 20 closed-ended questions related to AI applications in healthcare. Knowledge was assessed using multiple-choice questions, attitude through a 5-point Likert scale, and practice through adoption- and frequency-based responses. Scores were categorised into good, moderate, and poor levels using predefined cut-offs. Descriptive statistics, chi-square tests, and correlation analyses were performed.
RESULTS: A total of 176 students participated, including 118 females (67.0%) and 58 males (33.0%). Good knowledge regarding AI applications was observed in 109 participants (61.9%), while 126 participants (71.6%) demonstrated a positive attitude toward AI integration in clinical decision-making. However, only 87 participants (49.4%) reported adequate practical exposure to AI-based healthcare tools. Significant associations were observed between knowledge and attitude (χ²=8.14; p=0.004) and between knowledge and practice levels (χ²=11.27; p=0.001). A moderate positive correlation was identified between knowledge and practice scores (r=0.56; p<0.001). Approximately 119 participants (67.6%) believed AI could improve healthcare affordability and accessibility. Ethical concerns, data privacy issues, and lack of practical training were identified as major barriers to AI adoption.
CONCLUSIONS: The study demonstrates a knowledge-practice gap among future pharmacists regarding AI utilisation in healthcare. Although awareness and attitudes toward AI were favourable, limited practical exposure and concerns regarding ethics and data security hindered adoption. Integrating AI-focused education and hands-on training into pharmacy curricula may improve readiness for technology-driven, patient-centred, and cost-effective healthcare delivery.
Conference/Value in Health Info
2026-09, ISPOR Asia Pacific 2026, Bangkok, Thailand
Value in Health, Volume 55, Issue S1
Code
MT3
Topic
Medical Technologies
Disease
No Additional Disease & Conditions/Specialized Treatment Areas