首页|Recent Findings from Management Development Institute Has Provided New Informati on about Artificial Intelligence (Framework for Ai Adoption In Healthcare Sector : Integrated Delphi, Ism-micmac Approach)
Recent Findings from Management Development Institute Has Provided New Informati on about Artificial Intelligence (Framework for Ai Adoption In Healthcare Sector : Integrated Delphi, Ism-micmac Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Artific ial Intelligence. According to news originating from Gurgaon, India, by NewsRx c orrespondents, research stated, "Artificial Intelligence (AI) adoption is transf orming many industries, but its impact on the healthcare sector is life-changing . Recent studies and tests show that AI can deliver identical or better prognose s, diagnoses, and surgical outcomes than medical professionals." Our news journalists obtained a quote from the research from Management Developm ent Institute, "Healthcare sectors are adopting AI, and its applications are ref orming it by reducing expenditure and exceeding patient satisfaction. The dearth of AI advocacy and adoption has forfeited large annual opportunity costs for th e health industry and artificial intelligence engineers (AIE). There is a shorta ge of studies using quantitative models to test the barrier interrelationship an d its effect on AI adoption, especially from the perspective of a developing cou ntry like India. Therefore, this study explores the barriers to adopting AI in h ealthcare in India. A total of 250 barriers related to technology adoption are d etermined after thoroughly analyzing previous studies and several focus group di scussions (FGDs). Barriers are reduced to 16 most relevant barriers through mult iple Healthcare expert FGDs and the DELPHI method. Interpretive structural model ling (ISM) and crossimpact matrix multiplication applied to classification (MICM AC) are the analytical techniques used to classify the barriers into different i mpact levels and importance. The derived outcomes from the ISM and MICMAC method s illustrate that the unavailability of infrastructure and policy support and AI 's potential cybersecurity vulnerabilities are the predominant problems for AI a doption in healthcare."
GurgaonIndiaAsiaArtificial Intelli genceCybersecurityEmerging TechnologiesFinance and InvestmentInvestment and FinanceMachine LearningTechnologyManagement Development Institute
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