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All Journal ADI Journal on Recent Innovation (AJRI) Journal of Computer Science and Technology Application
Henry, Bintang Nandana
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Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Industrial & Manufacturing Engineering Other

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AI-Driven Big Data Solutions for Personalized Healthcare: Analyzing Patient Data to Improve Treatment Outcomes Rafika, Ageng Setiani; Faturahman, Adam; Henry, Bintang Nandana; Yulian, Firdaus Dwi; Hassan, Mohammed
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.61

Abstract

The advent of AI-driven big data solutions has transformed personalized healthcare by enabling the analysis of vast and complex patient datasets to optimize treatment outcomes. This study aims to evaluate the effectiveness of AI models in improving healthcare delivery through enhanced diagnostic accuracy, reduced processing times, and personalized treatment plans. The research utilizes AI models to process extensive patient data from electronic health records, wearable devices, and genetic information. The results show an impressive accuracy rate of 93%, a 25% reduction in diagnostic errors, and significant improvements in patient outcomes, including 72% of patients receiving more accurate diagnoses and 65% experiencing faster recovery. A comparison with traditional methods highlights the advantages of AI in scalability, efficiency, and reliability, offering a clear improvement over existing healthcare approaches. However, challenges such as data bias, ethical concerns, and scalability need to be addressed to en- sure the responsible application of AI in healthcare systems. In conclusion, this research provides valuable insights for healthcare organizations that aim to implement AI-driven solutions, fostering the advancement of patient care and encouraging innovation in the industry. The findings suggest that AI-powered big data solutions have the potential to revolutionize healthcare, improving diagnostic precision and treatment personalization, ultimately enhancing patient satisfaction and outcomes.
Cloud Computing and Artificial Intelligence for Secure and Sustainable Digital Transformation Andayani, Dwi; Lestari Santoso, Nuke Puji; Hua, Chua Toh; Henry, Bintang Nandana
ADI Journal on Recent Innovation (AJRI) Vol. 7 No. 2 (2026): March
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i2.1439

Abstract

The rapid growth of cloud computing and Artificial Intelligence (AI) has accel- erated digital transformation in business organizations while increasing concerns related to data security and sustainability. Ensuring secure and sustainable digital transformation has therefore become a strategic priority in the current digital economy. This study aims to examine the role of cloud computing and AI in supporting secure and sustainable digital transformation in business operations. This research adopts a quantitative approach by collecting survey data from 300 organizations that have implemented cloud computing and AI technologies. The data are analyzed using Structural Equation Modeling (SEM) to evaluate the relationships between cloud computing adoption AI capability digital security and sustainability performance. The results show that cloud computing adoption has a significant positive effect on secure digital transformation. AI capability also significantly enhances digital security and operational sustainability. Furthermore the integration of cloud computing and AI strengthens organizational readiness for sustainable digital transformation. The findings indicate that organizations leveraging both cloud computing and AI achieve higher levels of digital security and sustainability compared to those adopting these technologies independently. This study provides empirical evidence that integrated cloud and AI strategies are essential for achieving secure and sustainable digital transformation and offers practical implications for managers and policymakers in designing technology driven business strategies.
Co-Authors Dwi Andayani, Dwi Faturahman, Adam Hassan, Mohammed Hua, Chua Toh Lestari Santoso, Nuke Puji Rafika, Ageng Setiani Yulian, Firdaus Dwi