Bioengineering · 2024 · 770 citations · 99 references
ComputingComputer ScienceHealth InformaticsArtificial IntelligenceClinical Decision SupportHealth Information PrivacyEthical AssessmentHealth Information EthicsHealthcare AnalyticsHospital PrivacyMedical Technology AssessmentPatient PrivacyAi Implementation SuccessAi Integration StrategyAi TransparencyAi-driven InnovationDigital Health TransformationHealthcare PersonalizationHealthcare PrivacyHealthcare Systems Implementation
Healthcare systems worldwide face escalating costs, limited access, and a growing demand for personalized care, positioning AI as a key transformative force. The review aims to critically assess AI’s integration across healthcare domains, equipping researchers with a deeper understanding of its transformative potential. The review examines AI’s role in clinical decision‑making, hospital operations, medical imaging, and patient monitoring, illustrates domain‑specific transformations via case studies, and discusses assessment methods, ethical challenges, and data privacy and bias mitigation. The review encourages interdisciplinary dialogue among researchers, clinicians, and technologists to navigate AI implementation complexities, fostering AI‑driven solutions that prioritize ethical standards, equity, and a patient‑centered approach.
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI’s potential to mitigate these issues and aims to critically assess AI’s integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI’s transformative potential, this review equips researchers with a deeper understanding of AI’s current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.
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