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Kurniawan MH, Handiyani H, Nuraini T, Hariyati RTS, Sutrisno S. A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness. Ann Med 2024; 56:2302980. [PMID: 38466897 PMCID: PMC10930147 DOI: 10.1080/07853890.2024.2302980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 12/31/2023] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Utilizing artificial intelligence (AI) in chatbots, especially for chronic diseases, has become increasingly prevalent. These AI-powered chatbots serve as crucial tools for enhancing patient communication, addressing the rising prevalence of chronic conditions, and meeting the growing demand for supportive healthcare applications. However, there is a notable gap in comprehensive reviews evaluating the impact of AI-powered chatbot interventions in healthcare within academic literature. This study aimed to assess user satisfaction, intervention efficacy, and the specific characteristics and AI architectures of chatbot systems designed for chronic diseases. METHOD A thorough exploration of the existing literature was undertaken by employing diverse databases such as PubMed MEDLINE, CINAHL, EMBASE, PsycINFO, ACM Digital Library and Scopus. The studies incorporated in this analysis encompassed primary research that employed chatbots or other forms of AI architecture in the context of preventing, treating or rehabilitating chronic diseases. The assessment of bias risk was conducted using Risk of 2.0 Tools. RESULTS Seven hundred and eighty-four results were obtained, and subsequently, eight studies were found to align with the inclusion criteria. The intervention methods encompassed health education (n = 3), behaviour change theory (n = 1), stress and coping (n = 1), cognitive behavioural therapy (n = 2) and self-care behaviour (n = 1). The research provided valuable insights into the effectiveness and user-friendliness of AI-powered chatbots in handling various chronic conditions. Overall, users showed favourable acceptance of these chatbots for self-managing chronic illnesses. CONCLUSIONS The reviewed studies suggest promising acceptance of AI-powered chatbots for self-managing chronic conditions. However, limited evidence on their efficacy due to insufficient technical documentation calls for future studies to provide detailed descriptions and prioritize patient safety. These chatbots employ natural language processing and multimodal interaction. Subsequent research should focus on evidence-based evaluations, facilitating comparisons across diverse chronic health conditions.
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Affiliation(s)
- Moh Heri Kurniawan
- Doctoral Student, Faculty of Nursing, Universitas Indonesia, Depok, Indonesia
- Departement of Nursing, Faculty of Health, Universitas Aisyah Pringsewu, Kabupaten Pringsewu, Indonesia
| | - Hanny Handiyani
- Department of Nursing, Faculty of Nursing, Universitas Indonesia, Depok, Indonesia
| | - Tuti Nuraini
- Department of Nursing, Faculty of Nursing, Universitas Indonesia, Depok, Indonesia
| | | | - Sutrisno Sutrisno
- Departement of Nursing, Faculty of Health, Universitas Aisyah Pringsewu, Kabupaten Pringsewu, Indonesia
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Bertholet J, Al Hallaq H, Toma-Dasu I, Ingledew PA, Carlson DJ. Medical Physics Training and Education: Learning From the Past and Looking to the Future. Int J Radiat Oncol Biol Phys 2023; 117:1039-1044. [PMID: 37980131 DOI: 10.1016/j.ijrobp.2023.07.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 07/29/2023] [Indexed: 11/20/2023]
Affiliation(s)
- Jenny Bertholet
- Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Hania Al Hallaq
- Department of Radiation and Cellular Oncology, University of Chicago, Chicago, Illinois
| | - Iuliana Toma-Dasu
- Department of Physics, Medical Radiation Physics, Stockholm University, Stockholm, Sweden; Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden
| | - Paris Ann Ingledew
- Department of Radiation Oncology, British Columbia Cancer Agency, Vancouver, British Columbia, Canada
| | - David J Carlson
- Department of Therapeutic Radiology, Yale University, New Haven, Connecticut.
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Moore NS, McWilliam A, Aneja S. Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence. Semin Radiat Oncol 2023; 33:70-5. [PMID: 36517196 DOI: 10.1016/j.semradonc.2022.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Machine learning (ML) and artificial intelligence (AI) have demonstrated potential to improve the care of radiation oncology patients. Here we review recent advances applicable to the care of bladder cancer, with an eye towards studies that may suggest next steps in clinical implementation. Algorithms have been applied to clinical records, pathology, and radiology data to generate accurate predictive models for prognosis and clinical outcomes. AI has also shown increasing utility for auto-contouring and efficient creation of workflows involving multiple treatment plans. As technologies progress towards routine clinical use for bladder cancer patients, we also discuss emerging methods to improve interpretability and reliability of algorithms.
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Bin Sawad A, Narayan B, Alnefaie A, Maqbool A, Mckie I, Smith J, Yuksel B, Puthal D, Prasad M, Kocaballi AB. A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions. Sensors 2022; 22:2625. [PMID: 35408238 PMCID: PMC9003264 DOI: 10.3390/s22072625] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 03/12/2022] [Accepted: 03/24/2022] [Indexed: 02/06/2023]
Abstract
This paper reviews different types of conversational agents used in health care for chronic conditions, examining their underlying communication technology, evaluation measures, and AI methods. A systematic search was performed in February 2021 on PubMed Medline, EMBASE, PsycINFO, CINAHL, Web of Science, and ACM Digital Library. Studies were included if they focused on consumers, caregivers, or healthcare professionals in the prevention, treatment, or rehabilitation of chronic diseases, involved conversational agents, and tested the system with human users. The search retrieved 1087 articles. Twenty-six studies met the inclusion criteria. Out of 26 conversational agents (CAs), 16 were chatbots, seven were embodied conversational agents (ECA), one was a conversational agent in a robot, and another was a relational agent. One agent was not specified. Based on this review, the overall acceptance of CAs by users for the self-management of their chronic conditions is promising. Users’ feedback shows helpfulness, satisfaction, and ease of use in more than half of included studies. Although many users in the studies appear to feel more comfortable with CAs, there is still a lack of reliable and comparable evidence to determine the efficacy of AI-enabled CAs for chronic health conditions due to the insufficient reporting of technical implementation details.
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Dejohn CR, Grant SR, Seshadri M. Application of Machine Learning Methods to Improve the Performance of Ultrasound in Head and Neck Oncology: A Literature Review. Cancers (Basel) 2022; 14:665. [PMID: 35158932 PMCID: PMC8833587 DOI: 10.3390/cancers14030665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/19/2022] [Accepted: 01/26/2022] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Ultrasound (US) is a non-invasive imaging method that is routinely utilized in head and neck cancer patients to assess the anatomic extent of tumors, nodal and non-nodal neck masses and for imaging the salivary glands. In this review, we summarize the present evidence on whether the application of machine learning (ML) methods can potentially improve the performance of US in head and neck cancer patients. We found that published clinical literature on ML methods applied to US datasets was limited but showed evidence of improved diagnostic and prognostic performance. However, a majority of these studies were based on retrospective evaluation and conducted at a single center with a limited number of datasets. The conduct of multi-center studies could help better validate the performance of ML-based US radiomics and facilitate the integration of these approaches into routine clinical practice. Abstract Radiomics is a rapidly growing area of research within radiology that involves the extraction and modeling of high-dimensional quantitative imaging features using machine learning/artificial intelligence (ML/AI) methods. In this review, we describe the published clinical evidence on the application of ML methods to improve the performance of ultrasound (US) in head and neck oncology. A systematic search of electronic databases (MEDLINE, PubMed, clinicaltrials.gov) was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Of 15,080 initial articles identified, 34 studies were selected for in-depth analysis. Twenty-five out of 34 studies (74%) focused on the diagnostic application of US radiomics while 6 (18%) studies focused on response assessment and 3 (8%) studies utilized US radiomics for modeling normal tissue toxicity. Support vector machine (SVM) was the most commonly employed ML method (47%) followed by multivariate logistic regression (24%) and k-nearest neighbor analysis (21%). Only 11/34 (~32%) of the studies included an independent validation set. A majority of studies were retrospective in nature (76%) and based on single-center evaluation (85%) with variable numbers of patients (12–1609) and imaging datasets (32–1624). Despite these limitations, the application of ML methods resulted in improved diagnostic and prognostic performance of US highlighting the potential clinical utility of this approach.
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Charow R, Jeyakumar T, Younus S, Dolatabadi E, Salhia M, Al-Mouaswas D, Anderson M, Balakumar S, Clare M, Dhalla A, Gillan C, Haghzare S, Jackson E, Lalani N, Mattson J, Peteanu W, Tripp T, Waldorf J, Williams S, Tavares W, Wiljer D. Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review. JMIR Med Educ 2021; 7:e31043. [PMID: 34898458 PMCID: PMC8713099 DOI: 10.2196/31043] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 05/12/2023]
Abstract
BACKGROUND As the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education. OBJECTIVE With a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs' curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs' effectiveness. METHODS After the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career). RESULTS Of the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective. CONCLUSIONS This review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction.
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Affiliation(s)
- Rebecca Charow
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
| | | | | | - Elham Dolatabadi
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
| | - Mohammad Salhia
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Dalia Al-Mouaswas
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | | | - Sarmini Balakumar
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Megan Clare
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | | | - Caitlin Gillan
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Shabnam Haghzare
- University Health Network, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | | | | | - Jane Mattson
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Wanda Peteanu
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | - Tim Tripp
- University Health Network, Toronto, ON, Canada
| | - Jacqueline Waldorf
- Michener Institute of Education, University Health Network, Toronto, ON, Canada
| | | | - Walter Tavares
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Wilson Centre, Toronto, ON, Canada
| | - David Wiljer
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- University Health Network, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- CAMH Education, Centre for Addictions and Mental Health (CAMH), Toronto, ON, Canada
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Wiljer D, Salhia M, Dolatabadi E, Dhalla A, Gillan C, Al-Mouaswas D, Jackson E, Waldorf J, Mattson J, Clare M, Lalani N, Charow R, Balakumar S, Younus S, Jeyakumar T, Peteanu W, Tavares W. Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach. JMIR Res Protoc 2021; 10:e30940. [PMID: 34612839 PMCID: PMC8529463 DOI: 10.2196/30940] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/14/2021] [Accepted: 07/15/2021] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Significant investments and advances in health care technologies and practices have created a need for digital and data-literate health care providers. Artificial intelligence (AI) algorithms transform the analysis, diagnosis, and treatment of medical conditions. Complex and massive data sets are informing significant health care decisions and clinical practices. The ability to read, manage, and interpret large data sets to provide data-driven care and to protect patient privacy are increasingly critical skills for today's health care providers. OBJECTIVE The aim of this study is to accelerate the appropriate adoption of data-driven and AI-enhanced care by focusing on the mindsets, skillsets, and toolsets of point-of-care health providers and their leaders in the health system. METHODS To accelerate the adoption of AI and the need for organizational change at a national level, our multistepped approach includes creating awareness and capacity building, learning through innovation and adoption, developing appropriate and strategic partnerships, and building effective knowledge exchange initiatives. Education interventions designed to adapt knowledge to the local context and address any challenges to knowledge use include engagement activities to increase awareness, educational curricula for health care providers and leaders, and the development of a coaching and practice-based innovation hub. Framed by the Knowledge-to-Action framework, we are currently in the knowledge creation stage to inform the curricula for each deliverable. An environmental scan and scoping review were conducted to understand the current state of AI education programs as reported in the academic literature. RESULTS The environmental scan identified 24 AI-accredited programs specific to health providers, of which 11 were from the United States, 6 from Canada, 4 from the United Kingdom, and 3 from Asian countries. The most common curriculum topics across the environmental scan and scoping review included AI fundamentals, applications of AI, applied machine learning in health care, ethics, data science, and challenges to and opportunities for using AI. CONCLUSIONS Technologies are advancing more rapidly than organizations, and professionals can adopt and adapt to them. To help shape AI practices, health care providers must have the skills and abilities to initiate change and shape the future of their discipline and practices for advancing high-quality care within the digital ecosystem. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/30940.
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Affiliation(s)
- David Wiljer
- University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Centre for Addictions and Mental Health, CAMH Education, Toronto, ON, Canada
| | - Mohammad Salhia
- Michener Institute of Education at University Health Network, Toronto, ON, Canada
| | | | | | | | - Dalia Al-Mouaswas
- Michener Institute of Education at University Health Network, Toronto, ON, Canada
| | | | - Jacqueline Waldorf
- Michener Institute of Education at University Health Network, Toronto, ON, Canada
| | - Jane Mattson
- Michener Institute of Education at University Health Network, Toronto, ON, Canada
| | - Megan Clare
- Michener Institute of Education at University Health Network, Toronto, ON, Canada
| | | | - Rebecca Charow
- University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Sarmini Balakumar
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Michener Institute of Education at University Health Network, Toronto, ON, Canada
| | | | | | - Wanda Peteanu
- Michener Institute of Education at University Health Network, Toronto, ON, Canada
| | - Walter Tavares
- University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Wilson Centre, Toronto, ON, Canada
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Grunhut J, Wyatt ATM, Marques O. Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes. J Med Educ Curric Dev 2021; 8:23821205211036836. [PMID: 34778562 PMCID: PMC8580487 DOI: 10.1177/23821205211036836] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/13/2021] [Accepted: 07/13/2021] [Indexed: 05/05/2023]
Abstract
BACKGROUND As medicine and the delivery of healthcare enters the age of Artificial Intelligence (AI), the need for competent human-machine interaction to aid clinical decisions will rise. Medical students need to be sufficiently proficient in AI, its advantages to improve healthcare's expenses, quality, and access. Similarly, students must be educated about the shortfalls of AI such as bias, transparency, and liability. Overlooking a technology that will be transformative for the foreseeable future would place medical students at a disadvantage. However, there has been little interest in researching a proper method to implement AI in the medical education curriculum. This study aims to review the current literature that covers the attitudes of medical students towards AI, implementation of AI in the medical curriculum, and describe the need for more research in this area. METHODS An integrative review was performed to combine data from various research designs and literature. Pubmed, Medline (Ovid), GoogleScholar, and Web of Science articles between 2010 and 2020 were all searched with particular inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were successively pooled together, recorded, and analyzed quantitatively using a modified Hawkings evaluation form. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses was utilized to help improve reporting. RESULTS A total of 39 articles meeting inclusion criteria were identified. Primary assessments of medical students attitudes were identified (n = 5). Plans to implement AI in the curriculum for the purpose of teaching students about AI (n = 6) and articles reporting actual implemented changes (n = 2) were assessed. Finally, 26 articles described the need for more research on this topic or calling for the need of change in medical curriculum to anticipate AI in healthcare. CONCLUSIONS There are few plans or implementations reported on how to incorporate AI in the medical curriculum. Medical schools must work together to create a longitudinal study and initiative on how to successfully equip medical students with knowledge in AI.
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Affiliation(s)
- Joel Grunhut
- Charles E. Schmidt College of Medicine, Florida Atlantic University, USA
| | - Adam TM Wyatt
- Charles E. Schmidt College of Medicine, Florida Atlantic University, USA
| | - Oge Marques
- College of Engineering and Computer Science, Florida Atlantic University, USA
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