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Artificial intelligence in medical and healthcare service : applications and challenges

Sangeetha, V.; Mamatha, A.; Vaneeta, M.; Beena, K. - In: Modern healthcare marketing in the digital era, (pp. 116-130). 2024

Persistent link: https://www.econbiz.de/10014636371

A new adoption model for quality of experience assessed by radiologists using AI medical imaging technology

Anuchit Nirapai; Adisorn Leelasantitham - In: Journal of open innovation : technology, market, and … 10 (2024) 3, pp. 1-18

This study introduces a new adoption model for assessing the quality of experience (QoE) of radiologists using AI-based medical imaging technology. While AI has increasingly been used by radiologists for screening, diagnosis, and classification of medical images, previous investigations have...

Persistent link: https://www.econbiz.de/10015072126

Technological paradoxes and artificial intelligence implementation in healthcare : an application of paradox theory

Singh, Nidhi; Jain, Monika; Kamal, Muhammad Mustafa; … - In: Technological forecasting and social change : an … 198 (2024), pp. 1-18

Persistent link: https://www.econbiz.de/10015075671

Forecasting healthcare service volumes with machine learning algorithms

Yang, Dong-Hui; Zhu, Ke-Hui; Wang, Ruo-Nan - In: Journal of forecasting 43 (2024) 6, pp. 2358-2377

Persistent link: https://www.econbiz.de/10015110454

Quality and accountability of Large Language Models (LLMs) in healthcare in low- and middle-income countries (LMIC) : a simulated patient study using ChatGPT

Si, Yafei; Yang, Yuji; Wang, Xi; An, Ruopeng; Zu, Jiaqi; … - 2024

Using simulated patients to mimic nine established non-communicable and infectious diseases over 27 trials, we assess ChatGPT's effectiveness and reliability in diagnosing and treating common diseases in low- and middle-income countries. We find ChatGPT's performance varied within a single...

Persistent link: https://www.econbiz.de/10015062654

Quality and accountability of Large Language Models (LLMs) in healthcare in low- and middle-income countries (LMIC) : a simulated patient study using ChatGPT

Si, Yafei; Yang, Yuji; Wang, Xi; An, Ruopeng; Zu, Jiaqi; … - 2024

Persistent link: https://www.econbiz.de/10015045045

An innovative patient clustering method using data envelopment Analysis-Discriminant analysis and artificial neural networks : a case study in healthcare systems

Yousefi, Saeed; Saen, Reza Farzipoor; Shabanpour, Hadi; … - In: Socio-economic planning sciences : the international … 95 (2024), pp. 1-11

Persistent link: https://www.econbiz.de/10015101830

Challenges of AI adoption in the UAE healthcare

Al Badi, Fatma Khamis; Alhosani, Khawla Ali; Jabeen, Fauzia - In: Vision : the journal of business perspective 26 (2022) 2, pp. 193-207

Persistent link: https://www.econbiz.de/10013256813

A data-driven approach to shared decision-making in a healthcare environment

Singh, Sudhanshu; Verma, Rakesh; Koul, Saroj - In: Opsearch : journal of the Operational Research Society … 59 (2022) 2, pp. 732-746

Persistent link: https://www.econbiz.de/10013273224

Data driven approaches for healthcare : machine learning for identifying high utilizers

Yang, Chengliang; Delcher, Chris; Shenkman, Elizabeth; … - 2020

Introduction. Overview of Healthcare Data. Machine Learning Modeling from Healthcare Data. Machine Learning Modeling from Healthcare Data. Descriptive Analysis of High Utlizers. Residuals Analysis for Identifying High Utilizers.Machine Learning Results for High Utilizers.

Persistent link: https://www.econbiz.de/10013182378