Year: 2026 | Month: April-June | Volume: 11 | Issue: 2 | Pages: 11-21
DOI: https://doi.org/10.52403/gijhsr.20260202
Responsible Artificial Intelligence in Healthcare: A Framework for Ethical, Transparent, and Reliable Clinical Decision Support
Vinutha Ragavaiah Sethupathy Sarma1, Munawar Ali Ahmed2, Aahana Dash3
1Saveetha Dental College and Hospital
2Niles North High School IL, USA
3North Forsyth High School GA, USA
Corresponding Author: Vinutha Ragavaiah Sethupathy Sarma
ABSTRACT
By improving patient monitoring, diagnosis, treatment planning, and resource allocation, artificial intelligence is transforming healthcare. Healthcare environments are distinguished by huge volumes of heterogeneous data from wearable medical devices, medical imaging systems, laboratory information platforms, and electronic health records. AI techniques, such as machine learning through deep neural networks, help clinicians make more effective clinical decisions by facilitating the identification of abnormalities, the prediction of sickness likelihood, and the uncovering of concealed patterns across datasets. This paper reviews the related literature for the purpose of ascertaining the situation at present with respect to the application of AI in the healthcare sector and discusses how AI-driven solutions may lead to improved patient outcomes while decreasing the clinical burden.
It has been suggested that a layered Responsible AI deployment framework should focus on data quality, transparency, interpretability, fairness, integration into clinical workflows, and continuous monitoring of the system to make certain that such AI systems are reliable, ethical, and operationally dependable. The described experiment and evaluation process shall be complemented by performance enhancement, testing in the real world, and model training. The writers have studied algorithmic bias reduction, privacy, responsibility, and regulatory compliance from the operational and policy viewpoint. Limitations include resistance to acceptance, interoperability problems, and generalization challenges. Our research has contributed significantly by demonstrating the responsible and well-managed deployment of AI in order to support more accurate, patient-centered, and accessible healthcare delivery.
Keywords: Machine learning, electronic health records, medical imaging, patient safety, ethical AI, health care analytics, clinical decision support systems, artificial intelligence, and health care innovation.