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.

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

Social media is emerging as a new avenue for hospitals and patients to solicit input on the quality of care. However, social media data is unstructured and enormous in volume. Moreover, no empirical research on the use of social media data and perceived hospital quality of care based on patient online reviews has been performed in Malaysia. The purpose of this study was to investigate the determinants of positive sentiment expressed in hospital Facebook reviews in Malaysia, as well as the association between hospital accreditation and sentiments expressed in Facebook reviews. From 2017 to 2019, we retrieved comments from 48 official public hospitals’ Facebook pages. We used machine learning to build a sentiment analyzer and service quality (SERVQUAL) classifier that automatically classifies the sentiment and SERVQUAL dimensions. We utilized logistic regression analysis to determine our goals. We evaluated a total of 1852 reviews and our machine learning sentiment analyzer detected 72.1% of positive reviews and 27.9% of negative reviews. We classified 240 reviews as tangible, 1257 reviews as trustworthy, 125 reviews as responsive, 356 reviews as assurance, and 1174 reviews as empathy using our machine learning SERVQUAL classifier. After adjusting for hospital characteristics, all SERVQUAL dimensions except Tangible were associated with positive sentiment. However, no significant relationship between hospital accreditation and online sentiment was discovered. Facebook reviews powered by machine learning algorithms provide valuable, real-time data that may be missed by traditional hospital quality assessments. Additionally, online patient reviews offer a hitherto untapped indication of quality that may benefit all healthcare stakeholders. Our results confirm prior studies and support the use of Facebook reviews as an adjunct method for assessing the quality of hospital services in Malaysia.

Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews.

Although identifying high-cost inpatients with ischemic heart disease (IHD) at the point of admission is helpful for timely intervention and reducing costs, it is a difficult task due to the limited data available at such an early stage of treatment. The aim of this study was to tackle this challenge by integrating network analytics with machine learning. In this study, we first constructed two networks including Phenotypic Comorbidity Network and Distance-based Disease-Cost Network based on the hospital discharge records (HDR) of Chengdu, China during 2015–2017. Based on the two networks, three novel network features were generated to capture the potential relationships between comorbidities and high healthcare costs. Six machine learning models (Logistic Regression, Decision Tree, Neural Network, Random Forest, XGBoost, and LightGBM) with different input features (network and non-network features) were developed to compare their performance using HDR of 323,907 admissions for IHD between 2018 and 2019. Our results showed that LightGBM with the input feature set including network and non-network features achieved the best area under the receiver-operating characteristic curve (AUC) of 0.900. Network features were the most significant features accounting for the majority (47.4%) of feature importance. Models using network features provided more accurate prediction compared with those using only non-network features. In conclusion, the proposed approach combining network analysis and machine learning is useful for predicting high-cost inpatients with IHD at an early stage of admission, which highlights the potential value of developing and disseminating network-based machine learning in healthcare field. 

Early prediction of high-cost inpatients with ischemic heart disease using network analytics and machine learning

A data-driven Bayesian belief network model for exploring patient experience drivers in healthcare sector

The end goal of technological advancement used in crisis response and recovery is to prevent, reduce or mitigate the impact of a crisis, thereby enhancing sustainable recovery. Advanced technological approaches such as social media, machine learning (ML), social network analysis (SNA), and big data are vital to a sustainable crisis management decisions and communication. This study selects 28 articles via a systematic process that focuses on ML, SNA, and related technological tools to understand how these tools are shaping crisis management and decision making. The analysis shows the significance of these tools in advancing sustainable crisis management to support decision making, information management, communication, collaboration and cooperation, location-based services, community resilience, situational awareness, and social position. Moreover, the findings noted that managing diverse outreach information and communication is increasingly essential. In addition, the study indicates why big data and language, cross-platform support, and dataset lacking are emerging concerns for sustainable crisis management. Finally, the study contributes to how advanced technological solutions effectively affect crisis response, communication, decision making, and overall crisis management.

https://www.mdpi.com/2071-1050/14/10/5854/pdf?version=1652338403

How Advanced Technological Approaches Are Reshaping Sustainable Social Media Crisis Management and Communication: A Systematic Review

https://academicworks.medicine.hofstra.edu/cgi/viewcontent.cgi?article=8425&context=articles

An approach to predicting patient experience through machine learning and social network analysis

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An approach to predicting patient experience through machine learning and social network analysis