Cargando…

A Review on Human–AI Interaction in Machine Learning and Insights for Medical Applications

Objective: To provide a human–Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature high...

Descripción completa

Detalles Bibliográficos
Autores principales:	Maadi, Mansoureh, Akbarzadeh Khorshidi, Hadi, Aickelin, Uwe
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	MDPI 2021
Materias:	Review
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926732/ https://www.ncbi.nlm.nih.gov/pubmed/33671609 http://dx.doi.org/10.3390/ijerph18042121

Ejemplares similares

Evaluation of the Early Intervention Physiotherapist Framework for Injured Workers in Victoria, Australia: Data Analysis Follow-Up
por: Akbarzadeh Khorshidi, Hadi, et al.
Publicado: (2023)

A hybrid mathematical modelling approach for energy generation from hazardous waste during the COVID-19 pandemic
por: Valizadeh, Jaber, et al.
Publicado: (2021)

Energy valley optimizer: a novel metaheuristic algorithm for global and engineering optimization
por: Azizi, Mahdi, et al.
Publicado: (2023)

Shape and size optimization of truss structures by Chaos game optimization considering frequency constraints
por: Azizi, Mahdi, et al.
Publicado: (2022)

AI and machine learning
por: Rahman, Was
Publicado: (2020)

AI and Machine Learning for Coders
por: Moroney, Laurence
Publicado: (2020)

Self-Service AI with Power BI Desktop: Machine Learning Insights for Business
por: Ehrenmueller-Jensen, Markus
Publicado: (2020)

Applications of AI, machine vision and robotics
por: Boyer, Kim, et al.
Publicado: (1995)

Explainable AI: A Review of Machine Learning Interpretability Methods
por: Linardatos, Pantelis, et al.
Publicado: (2020)

AI enhanced collaborative human-machine interactions for home-based telerehabilitation
por: Le, Hoang H, et al.
Publicado: (2023)

Integration of AI and Machine Learning in Radiotherapy QA
por: Chan, Maria F., et al.
Publicado: (2020)

Special Issue on Machine Learning and AI for Sensors
por: Pintelas, Panagiotis, et al.
Publicado: (2023)

A review of explainable AI in the satellite data, deep machine learning, and human poverty domain
por: Hall, Ola, et al.
Publicado: (2022)

Edge Machine Learning for AI-Enabled IoT Devices: A Review
por: Merenda, Massimo, et al.
Publicado: (2020)

The BSM-AI project: SUSY-AI–generalizing LHC limits on supersymmetry with machine learning
por: Caron, Sascha, et al.
Publicado: (2017)

Security with AI and machine learning: using advanced tools to improve application security at the edge
por: Gil, Laurent, et al.
Publicado: (2018)

Investigating mathematical models of immuno-interactions with early-stage cancer under an agent-based modelling perspective
por: Figueredo, Grazziela P, et al.
Publicado: (2013)

Pragmatic AI: an introduction to cloud-based machine learning
por: Gift, Noah
Publicado: (2018)

Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework
por: van der Vegt, Anton H, et al.
Publicado: (2023)

Human–machine teaming is key to AI adoption: clinicians’ experiences with a deployed machine learning system
por: Henry, Katharine E., et al.
Publicado: (2022)

Machine Learning for Medical Applications
por: Zhou, Huiyu, et al.
Publicado: (2015)

Designing AI Systems With Human-Machine Teams
por: Saenz, Maria
Publicado: (2020)

Interpretable AI for bio-medical applications
por: Sathyan, Anoop, et al.
Publicado: (2022)

Untangling the complexity of multimorbidity with machine learning
por: Hassaine, Abdelaali, et al.
Publicado: (2020)

motilitAI: A machine learning framework for automatic prediction of human sperm motility
por: Ottl, Sandra, et al.
Publicado: (2022)

Disembodied AI and the limits to machine understanding of students' embodied interactions
por: Nathan, Mitchell J.
Publicado: (2023)

Diabetes prediction using machine learning and explainable AI techniques
por: Tasin, Isfafuzzaman, et al.
Publicado: (2022)

Machine learning at the edge for AI-enabled multiplexed pathogen detection
por: Ganjalizadeh, Vahid, et al.
Publicado: (2023)

Spinal Cord Injury AIS Predictions Using Machine Learning
por: Kapoor, Dhruv, et al.
Publicado: (2023)

Practical automated machine learning on Azure: using Azure machine learning to quickly build AI solutions
por: Mukunthu, Deepak, et al.
Publicado: (2019)

Machine Learning and Deep Learning based AI Tools for Development of Diagnostic Tools
por: Lepakshi, Vijayalakshmi A.
Publicado: (2022)

Explanatory machine learning for justified trust in human-AI collaboration: Experiments on file deletion recommendations
por: Göbel, Kyra, et al.
Publicado: (2022)

Application of machine learning in understanding atherosclerosis: Emerging insights
por: Munger, Eric, et al.
Publicado: (2021)

Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes
por: Panesar, Arjun
Publicado: (2020)

Machine learning and AI for healthcare: big data for improved health outcomes
por: Panesar, Arjun
Publicado: (2019)

Modern approaches in machine learning and cognitive science: latest trends in AI
por: Gunjan, Vinit Kumar, et al.
Publicado: (2020)

Developments in Transduction, Connectivity and AI/Machine Learning for Point-of-Care Testing
por: O’Sullivan, Shane, et al.
Publicado: (2019)

Looking beyond the hype: Applied AI and machine learning in translational medicine
por: Toh, Tzen S., et al.
Publicado: (2019)

Systematic Mapping Study of AI/Machine Learning in Healthcare and Future Directions
por: Parashar, Gaurav, et al.
Publicado: (2021)

More than meets the AI: The possibilities and limits of machine learning in olfaction
por: Barwich, Ann-Sophie, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_p2b484olp0tmbjmmge4bfi3ms7