1
|
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] [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.
Collapse
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
| |
Collapse
|
2
|
Grover S, Court L, Amoo-Mitchual S, Longo J, Rodin D, Scott AA, Lievens Y, Yap ML, Abdel-Wahab M, Lee P, Harsdorf E, Khader J, Jia X, Dosanjh M, Elzawawy A, Ige T, Pomper M, Pistenmaa D, Hardenbergh P, Petereit DG, Sargent M, Cina K, Li B, Anacak Y, Mayo C, Prattipati S, Lasebikan N, Rendle K, O'Brien D, Wendling E, Coleman CN. Global Workforce and Access: Demand, Education, Quality. Semin Radiat Oncol 2024; 34:477-493. [PMID: 39271284 DOI: 10.1016/j.semradonc.2024.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
There has long existed a substantial disparity in access to radiotherapy globally. This issue has only been exacerbated as the growing disparity of cancer incidence between high-income countries (HIC) and low and middle-income countries (LMICs) widens, with a pronounced increase in cancer cases in LMICs. Even within HICs, iniquities within local communities may lead to a lack of access to care. Due to these trends, it is imperative to find solutions to narrow global disparities. This requires the engagement of a diverse cohort of stakeholders, including working professionals, non-governmental organizations, nonprofits, professional societies, academic and training institutions, and industry. This review brings together a diverse group of experts to highlight critical areas that could help reduce the current global disparities in radiation oncology. Advancements in technology and treatment, such as artificial intelligence, brachytherapy, hypofractionation, and digital networks, in combination with implementation science and novel funding mechanisms, offer means for increasing access to care and education globally. Common themes across sections reveal how utilizing these new innovations and strengthening collaborative efforts among stakeholders can help improve access to care globally while setting the framework for the next generation of innovations.
Collapse
Affiliation(s)
- Surbhi Grover
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Botswana-University of Pennsylvania Partnership, Gaborone, Botswana.
| | - Laurence Court
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center
| | - Sheldon Amoo-Mitchual
- Botswana-University of Pennsylvania Partnership, Gaborone, Botswana; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - John Longo
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI
| | - Danielle Rodin
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada; Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, ON, Canada; Global Cancer Program, Princess Margaret Cancer Centre, Toronto, ON, Canada
| | | | - Yolande Lievens
- Department of Radiation Oncology, Ghent University Hospital, Belgium; Ghent University, Ghent, Belgium
| | - Mei Ling Yap
- Liverpool and Macarthur Cancer Therapy Centres, Western Sydney University, Campbelltown, New South Wales, Australia; The George Institute for Global Health, UNSW Sydney, Barangaroo, NSW, Australia; Collaboration for Cancer Outcomes, Research and Evaluation (CCORE), Ingham Institute, UNSW Sydney, Liverpool, NSW, Australia
| | - May Abdel-Wahab
- Division of Human Health, International Atomic Energy Agency, Vienna, Austria
| | - Peter Lee
- Division of Human Health, International Atomic Energy Agency, Vienna, Austria
| | - Ekaterina Harsdorf
- Division of Human Health, International Atomic Energy Agency, Vienna, Austria
| | - Jamal Khader
- Radiation Oncology Department, King Hussein Cancer Center, Amman, Jordan
| | - Xun Jia
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD
| | - Manjit Dosanjh
- ICEC, CERN, Geneva, Switzerland; University of Oxford, Oxford, UK
| | - Ahmed Elzawawy
- Department of Clinical Oncology, Suez Canal University, Ismailia, Egypt; Alsoliman Clinical and Radiation Oncology Center, Port Said, Egypt
| | | | - Miles Pomper
- James Martin Center for Nonproliferation Studies, Washington, DC; ICEC, International Cancer Expert Corps, Washington, DC
| | | | | | - Daniel G Petereit
- Monument Health Cancer Care Institute Rapid City, South Dakota; Avera Research Institute, Sioux Falls, SD
| | | | | | - Benjamin Li
- University of Washington, Seattle, WA; Fred Hutch Cancer Center, Seattle, WA
| | - Yavuz Anacak
- Department of Radiation Oncology, Ege University, Faculty of Medicine, Izmir, Turkey
| | - Chuck Mayo
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
| | | | - Nwamaka Lasebikan
- Department of Radiation and Clinical Oncology, University of Nigeria Teaching Hospital, Enugu, Nigeria
| | - Katharine Rendle
- Department of Family Medicine & Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Donna O'Brien
- ICEC, International Cancer Expert Corps, Washington, DC
| | | | - C Norman Coleman
- ICEC, International Cancer Expert Corps, Washington, DC; Radiation Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD
| |
Collapse
|
3
|
Wen F, Chen Z, Wang X, Dou M, Yang J, Yao Y, Shen Y. Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application. J Appl Clin Med Phys 2024:e14482. [PMID: 39120487 DOI: 10.1002/acm2.14482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 05/30/2024] [Accepted: 07/09/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed for clinical target volumes (CTVs) of intact and postoperative prostate cancer. METHODS Computed tomography (CT) data sets of 197 prostate cancer patients were collected. Two auto-delineation models were built for radical radiotherapy and postoperative radiotherapy of prostate cancer respectively, and each model included CTVn for pelvic lymph nodes and CTVp for prostate tumors or prostate tumor beds. RESULTS In the radical radiotherapy model, the volumetric dice (VD) coefficient of CTVn calculated by AI, was higher than that of the one delineated by the junior physicians (0.85 vs. 0.82, p = 0.018); In the postoperative radiotherapy model, the quantitative parameter of CTVn and CTVp, counted by AI, was better than that of the junior physicians. The median delineation time for AI was 0.23 min in the postoperative model and 0.26 min in the radical model, which were significantly shorter than those of the physicians (50.40 and 45.43 min, respectively, p < 0.001). The correction time of the senior physician for AI was much shorter compared with that for the junior physicians in both models (p < 0.001). CONCLUSION Using deep learning and attention mechanism, a highly consistent and time-saving contouring model was built for CTVs of pelvic lymph nodes and prostate tumors or prostate tumor beds for prostate cancer, which also might be a good approach to train junior radiation oncologists.
Collapse
Affiliation(s)
- Feng Wen
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Abdominal Oncology Ward, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhebin Chen
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Sichuan, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xin Wang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Abdominal Oncology Ward, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Meng Dou
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Sichuan, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jialuo Yang
- Department of Medicine Oncology, Shifang people's Hospital, Shifang, China
| | - Yu Yao
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Sichuan, Chengdu, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yali Shen
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Abdominal Oncology Ward, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
4
|
Hurkmans C, Bibault JE, Brock KK, van Elmpt W, Feng M, David Fuller C, Jereczek-Fossa BA, Korreman S, Landry G, Madesta F, Mayo C, McWilliam A, Moura F, Muren LP, El Naqa I, Seuntjens J, Valentini V, Velec M. A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy. Radiother Oncol 2024; 197:110345. [PMID: 38838989 DOI: 10.1016/j.radonc.2024.110345] [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: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND AND PURPOSE Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap. METHODS AND MATERIALS A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended. RESULTS The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated. CONCLUSION A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.
Collapse
Affiliation(s)
- Coen Hurkmans
- Department of Radiation Oncology, Catharina Hospital, Eindhoven, the Netherlands; Department of Electrical Engineering, Technical University Eindhoven, Eindhoven, the Netherlands.
| | | | - Kristy K Brock
- Departments of Imaging Physics and Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, the Netherlands
| | - Mary Feng
- University of California San Francisco, San Francisco, CA, USA
| | - Clifton David Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, TX
| | - Barbara A Jereczek-Fossa
- Dept. of Oncology and Hemato-oncology, University of Milan, Milan, Italy; Dept. of Radiation Oncology, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Stine Korreman
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Partner Site Munich, a Partnership between DKFZ and LMU University Hospital Munich, Germany; Bavarian Cancer Research Center (BZKF), Partner Site Munich, Munich, Germany
| | - Frederic Madesta
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Chuck Mayo
- Institute for Healthcare Policy and Innovation, University of Michigan, USA
| | - Alan McWilliam
- Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Filipe Moura
- CrossI&D Lisbon Research Center, Portuguese Red Cross Higher Health School Lisbon, Portugal
| | - Ludvig P Muren
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Jan Seuntjens
- Princess Margaret Cancer Centre, Radiation Medicine Program, University Health Network & Departments of Radiation Oncology and Medical Biophysics, University of Toronto, Toronto, Canada
| | - Vincenzo Valentini
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | - Michael Velec
- Radiation Medicine Program, Princess Margaret Cancer Centre and Department of Radiation Oncology, University of Toronto, Toronto, Canada
| |
Collapse
|
5
|
Arif WM. Radiologic Technology Students' Perceptions on Adoption of Artificial Intelligence Technology in Radiology. Int J Gen Med 2024; 17:3129-3136. [PMID: 39049835 PMCID: PMC11268710 DOI: 10.2147/ijgm.s465944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 07/11/2024] [Indexed: 07/27/2024] Open
Abstract
Study Purpose This study aims to analyze radiologic technology student's perceptions of artificial intelligence (AI) and its applications in radiology. Methods A quantitative cross-sectional survey was conducted. A pre-validated survey questionnaire with 17 items related to students perceptions of AI and its applications was used. The sample included radiologic technology students from three universities in Saudi Arabia. The survey was conducted online for several weeks, resulting in a sample of 280 radiologic technology students. Results Of the participants, 63.9% were aware of AI and its applications. T-tests revealed a statistically significant difference (p = 0.0471) between genders with male participants reflecting slightly higher AI awareness than female participants. Regarding the choice of radiology as specialization, 35% of the participants stated that they would not choose radiology, whereas 65% preferred it. Approximately 56% of the participants expressed concerns about the potential replacement of radiology technologists with AI, and 62.1% strongly agreed on the necessity of incorporating known ethical principles into AI. Conclusion The findings reflect a positive evaluation of the applications of this technology, which is attributed to its essential support role. However, tailored education and training programs are necessary to prepare future healthcare professionals for the increasing role of AI in medical sciences.
Collapse
Affiliation(s)
- Wejdan M Arif
- King Saud University, College of Applied Medical Sciences, Department of Radiological Sciences, Riyadh, Saudi Arabia
| |
Collapse
|
6
|
Purkayastha S, Shalu H, Gutman D, Holodny A, Modak S, Basu E, Kushner B, Kramer K, Haque S, Stember JN. Evolutionary Strategies AI Addresses Multiple Technical Challenges in Deep Learning Deployment: Proof-of-Principle Demonstration for Neuroblastoma Brain Metastasis Detection. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01165-z. [PMID: 38886289 DOI: 10.1007/s10278-024-01165-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 05/09/2024] [Accepted: 06/03/2024] [Indexed: 06/20/2024]
Abstract
Two significant obstacles hinder the advancement of Radiology AI. The first is the challenge of overfitting, where small training data sets can result in unreliable outcomes. The second challenge is the need for more generalizability, the lack of which creates difficulties in implementing the technology across various institutions and practices. A recent innovation, deep neuroevolution (DNE), has been introduced to tackle the overfitting issue by training on small data sets and producing accurate predictions. However, the generalizability of DNE has yet to be proven. This paper strives to overcome this barrier by demonstrating that DNE can achieve satisfactory results in diverse external validation sets. The main innovation of the work is thus showing that DNE can generalize to varied outside data. Our example use case is predicting brain metastasis from neuroblastoma, emphasizing the importance of AI with limited data sets. Despite image collection and labeling advancements, rare diseases will always constrain data availability. We optimized a convolutional neural network (CNN) with DNE to demonstrate generalizability. We trained the CNN with 60 MRI images and tested it on a separate diverse collection of images from over 50 institutions. For comparison, we also trained with the more traditional stochastic gradient descent (SGD) method, with the two variants of (1) training from scratch and (2) transfer learning. Our results show that DNE demonstrates excellent generalizability with 97% accuracy on the heterogeneous testing set, while neither form of SGD could reach 60% accuracy. DNE's ability to generalize from small training sets to external and diverse testing sets suggests that it or similar approaches may play an integral role in improving the clinical performance of AI.
Collapse
Affiliation(s)
- Subhanik Purkayastha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Hrithwik Shalu
- Department of Aerospace Engineering, Indian Institute of Technology Madras, Chennai, India, 600036
| | - David Gutman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Andrei Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Shakeel Modak
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Ellen Basu
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Brian Kushner
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Kim Kramer
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Sofia Haque
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Joseph N Stember
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
| |
Collapse
|
7
|
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] [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.
| |
Collapse
|
8
|
Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence. Semin Radiat Oncol 2023; 33:70-75. [PMID: 36517196 DOI: 10.1016/j.semradonc.2022.10.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [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.
Collapse
|
9
|
A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions. SENSORS 2022; 22:s22072625. [PMID: 35408238 PMCID: PMC9003264 DOI: 10.3390/s22072625] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [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.
Collapse
|
10
|
Application of Machine Learning Methods to Improve the Performance of Ultrasound in Head and Neck Oncology: A Literature Review. Cancers (Basel) 2022; 14:cancers14030665. [PMID: 35158932 PMCID: PMC8833587 DOI: 10.3390/cancers14030665] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [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.
Collapse
|
11
|
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 MEDICAL EDUCATION 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] [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.
Collapse
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
| |
Collapse
|
12
|
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] [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.
Collapse
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
| |
Collapse
|
13
|
Grunhut J, Wyatt ATM, Marques O. Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes. JOURNAL OF MEDICAL EDUCATION AND CURRICULAR DEVELOPMENT 2021; 8:23821205211036836. [PMID: 34778562 PMCID: PMC8580487 DOI: 10.1177/23821205211036836] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [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.
Collapse
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
| |
Collapse
|