Application of AI in Nutrition
December 18, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ritu Ramakrishnan, Tianxiang Xing, Tianfeng Chen, Ming-Hao Lee, Jinzhu Gao
arXiv ID
2312.11569
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.IR
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In healthcare, artificial intelligence (AI) has been changing the way doctors and health experts take care of people. This paper will cover how AI is making major changes in the health care system, especially with nutrition. Various machine learning and deep learning algorithms have been developed to extract valuable information from healthcare data which help doctors, nutritionists, and health experts to make better decisions and make our lifestyle healthy. This paper provides an overview of the current state of AI applications in healthcare with a focus on the utilization of AI-driven recommender systems in nutrition. It will discuss the positive outcomes and challenges that arise when AI is used in this field. This paper addresses the challenges to develop AI recommender systems in healthcare, providing a well-rounded perspective on the complexities. Real-world examples and research findings are presented to underscore the tangible and significant impact AI recommender systems have in the field of healthcare, particularly in nutrition. The ongoing efforts of applying AI in nutrition lay the groundwork for a future where personalized recommendations play a pivotal role in guiding individuals toward healthier lifestyles.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted