Perceptions of Artificial Intelligence in Healthcare: A Cross-Sectional Need-Based Survey Among Healthcare Workers
Shivi Mishra, Rohit Singh, Nazia Nazir Zargar, Samiksha KhanujaObjectives:
Artificial intelligence (AI) based tools are rapidly entering clinical workflows. Understanding clinicians’ perceptions, the areas they expect to benefit from AI, desired competency levels, and ethical concerns are essential to plan training and safe implementation.
Material and Methods:
A cross-sectional, anonymized Google survey was distributed to healthcare workers at a medical college. Data were collected between 16 January 2026 and 23 January 2026 ( N = 99 respondents). The questionnaire asked about perceived benefits of AI, current clinical challenges that AI could help, workflow areas AI could optimize, why clinicians should understand AI, key ethical concerns, desired competency level, and one measurable outcome that AI training could improve.
Results:
Ninety-nine respondents from a wide range of specialties participated (most frequent specialties: General Surgery 8/99, Microbiology, Pediatrics, General Medicine each 5/99). Mean clinical experience was 10.6 years. The most selected potential benefits were earlier disease detection (74/99, 74.7%) and improved diagnostic accuracy (72/99, 72.7%). AI use in the optimization of clinical notes (85/99, 85.9%) and imaging interpretation (65/99, 65.7%) was documented. Desired AI competency levels were practical use/applying tools safely (46/99, 46.5%), advanced (designing/evaluating models) (40/99, 40.4%), and basic literacy (13/99, 13.1%). The leading ethical concerns were clinician accountability/liability (41/99, 41.4%), patient safety (31/99, 31.3%), transparency (22/99, 22.2%), bias (16/99, 16.2%), and data confidentiality (15/99, 15.2%).
Conclusion:
Clinicians across career stages expect AI to improve detection and diagnostic accuracy and wish for AI training that emphasizes practical, safe use and accountability.