From Data to Decisions: Transforming Healthcare Through AI-Powered Analytics | IJCT Volume 12 – Issue 6 | IJCT-V12I6P39
International Journal of Computer Techniques
ISSN 2394-2231
Author
P.Kameswararao,
D.Jaswanth Siva Nageswar
Abstract
The exponential growth of healthcare data, combined with advances in artificial intelligence and machine learning, has created unprecedented opportunities to transform clinical decision-making and patient care. This article examines how AI-driven decision support systems unlock the latent value of health data by enabling more accurate diagnoses, personalized treatment plans, and improved patient outcomes. We explore the technological foundations of these systems, analyze their clinical applications and demonstrated impact, and discuss the implementation challenges and ethical considerations that must be addressed. The evidence suggests that AI-driven decision support systems represent a paradigm shift in healthcare delivery, with the potential to enhance clinical effectiveness, reduce costs, and democratize access to expert-level medical insights. However, realizing this potential requires careful attention to data quality, algorithmic transparency, regulatory compliance, and the preservation of human clinical judgment. This review synthesizes current research and practice to provide a comprehensive framework for understanding and implementing AI-driven decision support in healthcare settings.
Keywords
Artificial Intelligence, Clinical Decision Support Systems, Healthcare Data Analytics, Machine Learning, Medical Informatics, Precision Medicine.
Conclusion
AI-driven clinical decision support systems represent a transformative opportunity to unlock the value of healthcare data and enhance the quality, efficiency, and equity of care delivery. These systems leverage advanced computational methods to extract insights from complex, multi-dimensional health data at scales beyond human capability. Demonstrated applications span diagnostic support, treatment optimization, risk prediction, and operational improvement, with empirical evidence of clinical benefit.
However, realizing this potential requires addressing substantial challenges. Technical requirements include robust infrastructure, high-quality data, and rigorous validation. Organizational factors encompass leadership support, clinician engagement, and workflow integration. Ethical considerations demand attention to algorithmic bias, transparency, privacy, and accountability. Regulatory frameworks must balance innovation with patient safety. Successful implementation requires coordinated efforts across technical, clinical, organizational, and policy domains.
The future of AI in healthcare is promising but not predetermined. Emerging technologies offer new capabilities, while integration with evolving care delivery models creates synergies. Research priorities include clinical validation, implementation optimization, methodological advancement, and ethical framework development. The vision of AI-augmented healthcare delivery, where human expertise is enhanced by computational intelligence, is increasingly achievable.
Ultimately, AI-driven decision support should be viewed not as replacing human clinical judgment but as augmenting it. The most effective implementations will preserve and enhance the human elements of healthcare while leveraging computational capabilities to manage complexity, reduce errors, and personalize care. As the healthcare community navigates this transformation, maintaining focus on improving patient outcomes, advancing health equity, and supporting clinician well-being will ensure that AI serves human flourishing. The successful unlocking of health data value through AI-driven decision support has the potential to fundamentally improve healthcare delivery and population health in the decades ahead.
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How to Cite This Paper
P.Kameswararao,
D.Jaswanth Siva Nageswar (2025). From Data to Decisions: Transforming
Healthcare Through AI-Powered Analytics. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
