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Gou F, Liu J, Xiao C, Wu J. Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence. Diagnostics (Basel) 2024; 14:1472. [PMID: 39061610 PMCID: PMC11275417 DOI: 10.3390/diagnostics14141472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024] Open
Abstract
With the improvement of economic conditions and the increase in living standards, people's attention in regard to health is also continuously increasing. They are beginning to place their hopes on machines, expecting artificial intelligence (AI) to provide a more humanized medical environment and personalized services, thus greatly expanding the supply and bridging the gap between resource supply and demand. With the development of IoT technology, the arrival of the 5G and 6G communication era, and the enhancement of computing capabilities in particular, the development and application of AI-assisted healthcare have been further promoted. Currently, research on and the application of artificial intelligence in the field of medical assistance are continuously deepening and expanding. AI holds immense economic value and has many potential applications in regard to medical institutions, patients, and healthcare professionals. It has the ability to enhance medical efficiency, reduce healthcare costs, improve the quality of healthcare services, and provide a more intelligent and humanized service experience for healthcare professionals and patients. This study elaborates on AI development history and development timelines in the medical field, types of AI technologies in healthcare informatics, the application of AI in the medical field, and opportunities and challenges of AI in the field of medicine. The combination of healthcare and artificial intelligence has a profound impact on human life, improving human health levels and quality of life and changing human lifestyles.
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Affiliation(s)
- Fangfang Gou
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Jun Liu
- The Second People's Hospital of Huaihua, Huaihua 418000, China
| | - Chunwen Xiao
- The Second People's Hospital of Huaihua, Huaihua 418000, China
| | - Jia Wu
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- Research Center for Artificial Intelligence, Monash University, Melbourne, Clayton, VIC 3800, Australia
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Singla A, Khanna R, Kaur M, Kelm K, Zaiane O, Rosenfelt CS, Bui TA, Rezaei N, Nicholas D, Reformat MZ, Majnemer A, Ogourtsova T, Bolduc F. Developing a Chatbot to Support Individuals With Neurodevelopmental Disorders: Tutorial. J Med Internet Res 2024; 26:e50182. [PMID: 38888947 PMCID: PMC11220430 DOI: 10.2196/50182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/27/2023] [Accepted: 04/19/2024] [Indexed: 06/20/2024] Open
Abstract
Families of individuals with neurodevelopmental disabilities or differences (NDDs) often struggle to find reliable health information on the web. NDDs encompass various conditions affecting up to 14% of children in high-income countries, and most individuals present with complex phenotypes and related conditions. It is challenging for their families to develop literacy solely by searching information on the internet. While in-person coaching can enhance care, it is only available to a minority of those with NDDs. Chatbots, or computer programs that simulate conversation, have emerged in the commercial sector as useful tools for answering questions, but their use in health care remains limited. To address this challenge, the researchers developed a chatbot named CAMI (Coaching Assistant for Medical/Health Information) that can provide information about trusted resources covering core knowledge and services relevant to families of individuals with NDDs. The chatbot was developed, in collaboration with individuals with lived experience, to provide information about trusted resources covering core knowledge and services that may be of interest. The developers used the Django framework (Django Software Foundation) for the development and used a knowledge graph to depict the key entities in NDDs and their relationships to allow the chatbot to suggest web resources that may be related to the user queries. To identify NDD domain-specific entities from user input, a combination of standard sources (the Unified Medical Language System) and other entities were used which were identified by health professionals as well as collaborators. Although most entities were identified in the text, some were not captured in the system and therefore went undetected. Nonetheless, the chatbot was able to provide resources addressing most user queries related to NDDs. The researchers found that enriching the vocabulary with synonyms and lay language terms for specific subdomains enhanced entity detection. By using a data set of numerous individuals with NDDs, the researchers developed a knowledge graph that established meaningful connections between entities, allowing the chatbot to present related symptoms, diagnoses, and resources. To the researchers' knowledge, CAMI is the first chatbot to provide resources related to NDDs. Our work highlighted the importance of engaging end users to supplement standard generic ontologies to named entities for language recognition. It also demonstrates that complex medical and health-related information can be integrated using knowledge graphs and leveraging existing large datasets. This has multiple implications: generalizability to other health domains as well as reducing the need for experts and optimizing their input while keeping health care professionals in the loop. The researchers' work also shows how health and computer science domains need to collaborate to achieve the granularity needed to make chatbots truly useful and impactful.
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Affiliation(s)
- Ashwani Singla
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - Ritvik Khanna
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - Manpreet Kaur
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - Karen Kelm
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - Osmar Zaiane
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | | | - Truong An Bui
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - Navid Rezaei
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - David Nicholas
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - Marek Z Reformat
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - Annette Majnemer
- School of Physical & Occupational Therapy, McGill University, Montreal, QC, Canada
| | - Tatiana Ogourtsova
- School of Physical & Occupational Therapy, McGill University, Montreal, QC, Canada
| | - Francois Bolduc
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
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Abu-Salih B, Alotaibi S. A systematic literature review of knowledge graph construction and application in education. Heliyon 2024; 10:e25383. [PMID: 38327411 PMCID: PMC10847940 DOI: 10.1016/j.heliyon.2024.e25383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 01/18/2024] [Accepted: 01/25/2024] [Indexed: 02/09/2024] Open
Abstract
In the dynamic landscape of modern education, the search for improved pedagogical methods, enriched learning experiences, and empowered educators remains a perpetual pursuit. In recent years, a remarkable technological innovation has asserted its dominance in education: Knowledge Graphs (KGs). These structured representations of knowledge are increasingly proving to be indispensable tools, fostering advancements driven by the growing recognition of their essential role in enriching personalised learning, curriculum design, concept mapping, and educational content recommendation systems. In this paper, a systematic literature review (SLR) has been conducted to comprehensively examine KG construction methodologies and their applications across five key domains in education. In each examined study, we highlight the specific KG functionalities, knowledge extraction techniques, knowledge base characteristics, resource requirements, evaluation criteria, and limitations. This paper distinguishes itself by offering a broad overview of KGs in education, analyzing state-of-the-art methodologies, and identifying research gaps and limitations, paving the way for future advancements.
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Affiliation(s)
- Bilal Abu-Salih
- King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan
| | - Salihah Alotaibi
- Information Systems Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), 11432, Riyadh, Saudi Arabia
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