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Ethical and regulatory challenges of AI technologies in healthcare: A narrative review.

作者信息

Mennella Ciro, Maniscalco Umberto, De Pietro Giuseppe, Esposito Massimo

机构信息

Institute for High-Performance Computing and Networking (ICAR) - Research National Council of Italy (CNR), Italy.

出版信息

Heliyon. 2024 Feb 15;10(4):e26297. doi: 10.1016/j.heliyon.2024.e26297. eCollection 2024 Feb 29.


DOI:10.1016/j.heliyon.2024.e26297
PMID:38384518
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10879008/
Abstract

Over the past decade, there has been a notable surge in AI-driven research, specifically geared toward enhancing crucial clinical processes and outcomes. The potential of AI-powered decision support systems to streamline clinical workflows, assist in diagnostics, and enable personalized treatment is increasingly evident. Nevertheless, the introduction of these cutting-edge solutions poses substantial challenges in clinical and care environments, necessitating a thorough exploration of ethical, legal, and regulatory considerations. A robust governance framework is imperative to foster the acceptance and successful implementation of AI in healthcare. This article delves deep into the critical ethical and regulatory concerns entangled with the deployment of AI systems in clinical practice. It not only provides a comprehensive overview of the role of AI technologies but also offers an insightful perspective on the ethical and regulatory challenges, making a pioneering contribution to the field. This research aims to address the current challenges in digital healthcare by presenting valuable recommendations for all stakeholders eager to advance the development and implementation of innovative AI systems.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb5/10879008/019e19940be8/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb5/10879008/5331ff72dcbf/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb5/10879008/14725a4ee19e/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb5/10879008/836a1637075d/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb5/10879008/e4e3f314fdd8/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb5/10879008/019e19940be8/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb5/10879008/5331ff72dcbf/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb5/10879008/14725a4ee19e/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb5/10879008/836a1637075d/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb5/10879008/e4e3f314fdd8/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9bb5/10879008/019e19940be8/gr005.jpg

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[1]
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[2]
A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions.

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[3]
Use of artificial intelligence in the detection of primary prostate cancer in multiparametric MRI with its clinical outcomes: a protocol for a systematic review and meta-analysis.

BMJ Open. 2023-8-22

[4]
A Review of the Role of Artificial Intelligence in Healthcare.

J Pers Med. 2023-6-5

[5]
Predicting the Onset of Diabetes with Machine Learning Methods.

J Pers Med. 2023-2-24

[6]
ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides.

Sci Rep. 2022-12-2

[7]
Using Artificial Intelligence-Enhanced Sensing and Wearable Technology in Sports Medicine and Performance Optimisation.

Sensors (Basel). 2022-9-13

[8]
Barriers to Use Artificial Intelligence Methodologies in Health Technology Assessment in Central and East European Countries.

Front Public Health. 2022

[9]
Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review.

IEEE Rev Biomed Eng. 2023

[10]
An artificial intelligence-based risk prediction model of myocardial infarction.

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