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THE CONCEPTS OF SOCIAL RESPONSIBILITY AND KNOWLEDGE MANAGEMENT IN HIGHER EDUCATION FOR SUSTAINABILITY |
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| รหัสดีโอไอ | |
| Creator | Lertlak Charoensombat |
| Title | THE CONCEPTS OF SOCIAL RESPONSIBILITY AND KNOWLEDGE MANAGEMENT IN HIGHER EDUCATION FOR SUSTAINABILITY |
| Contributor | Akkakorn Chaiyaphong |
| Publisher | คณะนิเทศศาสตร์ มหาวิทยาลัยราชภัฏรำไพพรรณี |
| Publication Year | 2568 |
| Journal Title | วารสารสหวิทยาการสังคมศาสตร์และการสื่อสาร |
| Journal Vol. | 8 |
| Journal No. | 1 |
| Page no. | 242-259 |
| Keyword | Corporate Social Responsibility, Knowledge Management, Sustainability, Higher Education |
| URL Website | https://so02.tci-thaijo.org/index.php/ISSC/index |
| Website title | https://so02.tci-thaijo.org/index.php/ISSC/index |
| ISSN | 2985-248X |
| Abstract | Introduction: Digital transformation has revolutionized healthcare, with artificial intelligence (AI) and machine learning (ML) enhancing diagnosis, patient management, and efficiency. Despite these advancements, concerns regarding data security, ethics, and system integration persist. This study examines the effectiveness of AI-driven decision support systems in improving diagnostic accuracy and reducing healthcare costs. Objectives: This research evaluates AI-based decision support tools in medical diagnosis, focusing on accuracy, efficiency, and cost reduction while identifying challenges in clinical implementation. Methods: A systematic review of AI applications in healthcare was conducted, alongside a case study of three hospitals. Data from patient records, diagnostic outcomes, and clinician feedback were analyzed. AI performance was assessed using sensitivity, specificity, and accuracy metrics, compared to traditional diagnostic methods. Results: AI-driven systems improved diagnostic accuracy by 15% and reduced diagnostic time by 30%. Cost savings of 20% in operational expenses were observed. However, challenges such as data privacy concerns and physician trust in AI recommendations emerged. Conclusion: AI-powered diagnostic tools enhance accuracy and efficiency while lowering costs. Addressing security issues and fostering AI-human collaboration is crucial for broader adoption. Future research should focus on transparent and ethical AI frameworks to ensure trust and regulatory compliance. |
