Authors :
Divya Gangaram Shinde
Volume/Issue :
Volume 11 - 2026, Issue 2 - February
Google Scholar :
https://tinyurl.com/3rfhh8fh
Scribd :
https://tinyurl.com/y4d6h33h
DOI :
https://doi.org/10.38124/ijisrt/26feb700
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
The current research aims to evaluate the integration of Artificial Intelligence (AI) within healthcare
environments, comparing the rates of integration and efficiency perceptions for both clinical professionals, namely doctors
and nurses, and non-clinical staff, covering administrative and billing personnel. Data for this research were collected
through a structured questionnaire that focused on the realities of day-to-day interactions with AI technologies,
professionals' confidence with AI-generated diagnoses, efficiency perceptions of AI integration, and the risks of
compromising patient data privacy.
It is clear from these findings that there are distinguishable differences between each group. The administrative workers
reported higher levels of adoption when it came to scheduling and billing, revealing some significant advantages to
operational efficiency. The clinical workers, on the other hand, reflected a sense of caution, concerns about diagnostic
accuracy and liability, and the absence of "human touch." Both groups, nonetheless, acknowledged AI as a force of evolution
in medicine and reinforced the necessity to establish guidelines and training.
This implies the increased rate at which administrative tasks are becoming automated, yet the "human-in-the-loop"
plays a vital role in decision-making. Training programs need to be implemented on a larger scale for the gap between
technological capacity and trust to be filled, so AI can be harnessed for support, not replacement, of medical decisionmaking.
Keywords :
Artificial Intelligence, Healthcare Adoption, Clinical or Administrative, AI Ethics, Efficiency in Workflow, Medical Diagnostics.
References :
- Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
- Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal.
- Jiang, F., et al. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology.
- He, J., et al. (2019). The Promise and Peril of Deep Learning in Radiology. Radiology: Artificial Intelligence.
- American Medical Association (AMA). (2023). Augmented Intelligence in Health Care.
- PwC Health Research Institute. (2022). AI in Healthcare: The Transformation is Just Beginning.
The current research aims to evaluate the integration of Artificial Intelligence (AI) within healthcare
environments, comparing the rates of integration and efficiency perceptions for both clinical professionals, namely doctors
and nurses, and non-clinical staff, covering administrative and billing personnel. Data for this research were collected
through a structured questionnaire that focused on the realities of day-to-day interactions with AI technologies,
professionals' confidence with AI-generated diagnoses, efficiency perceptions of AI integration, and the risks of
compromising patient data privacy.
It is clear from these findings that there are distinguishable differences between each group. The administrative workers
reported higher levels of adoption when it came to scheduling and billing, revealing some significant advantages to
operational efficiency. The clinical workers, on the other hand, reflected a sense of caution, concerns about diagnostic
accuracy and liability, and the absence of "human touch." Both groups, nonetheless, acknowledged AI as a force of evolution
in medicine and reinforced the necessity to establish guidelines and training.
This implies the increased rate at which administrative tasks are becoming automated, yet the "human-in-the-loop"
plays a vital role in decision-making. Training programs need to be implemented on a larger scale for the gap between
technological capacity and trust to be filled, so AI can be harnessed for support, not replacement, of medical decisionmaking.
Keywords :
Artificial Intelligence, Healthcare Adoption, Clinical or Administrative, AI Ethics, Efficiency in Workflow, Medical Diagnostics.