Future-Ready Doctors: A Cross-Sectional Study of Undergraduate Knowledge of Artificial Intelligence in Clinical Biochemistry

Background: Artificial intelligence (AI) is increasingly transforming healthcare delivery, particularly in laboratory medicine and clinical biochemistry. Despite its expanding applications, the preparedness of future medical professionals to engage with AI remains uncertain. Although several studies have evaluated awareness of AI among medical students globally, limited research has specifically focused on AI applications in clinical biochemistry and laboratory medicine in the Indian context. Aim: This study aims to appraise the knowledge, perceptions, and readiness regarding AI in clinical biochemistry among undergraduate MBBS students. Material and methods: A cross-sectional, questionnaire-based study was conducted over three months (September-December 2025) at Sri Devaraj Urs Medical College, following approval from the institutional ethics committee. A total of 268 MBBS students from Phase I to Phase III participated. Data was collected using a structured, self-administered Google form questionnaire (Google, Mountain View, CA, USA) covering demographics, knowledge, attitudes, readiness, and perceived barriers related to AI in clinical biochemistry. Data were analyzed using SPSS Statistics version 16 (IBM Corp. Released 2007. IBM SPSS Statistics for Windows, Version 16.0. Armonk, NY: IBM Corp.). Descriptive statistics were expressed as frequencies and percentages. Association between academic phase and responses was analyzed using the chi-square test, and effect size was assessed using Cramer’s V. A p-value of <0.05 was considered statistically significant. Results: Awareness of AI in healthcare among students was high (88.4%, n = 237); however, only 19% (n = 51) had received prior formal training in the use of AI. Knowledge of AI in clinical biochemistry increased progressively across academic phases, yet understanding of advanced laboratory applications remained limited. Students largely perceived AI as a supportive tool that enhances report accuracy, reduces errors, and improves laboratory turnaround time. The highest agreement was observed for the importance of AI knowledge in future medical practice (mean Likert score 4.06), whereas the concept of AI replacing laboratory professionals showed the lowest agreement (2.45). Ethical concerns such as data privacy and governance were widely recognized. More than half of the respondents (52.6%, n = 141) expressed willingness to undergo formal AI training. Conclusions: The findings highlight a significant gap between awareness and structured competency regarding AI among medical undergraduates. The need for early, phase-appropriate, and ethically grounded AI integration within the undergraduate medical curriculum is essential to prepare future physicians for an AI-enabled healthcare system.