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Naqvi HA, Delungahawatta T, Atarere JO, Bandaru SK, Barrow JB, Mattar MC. Evaluation of online chat-based artificial intelligence responses about inflammatory bowel disease and diet. Eur J Gastroenterol Hepatol 2024; 36:1109-1112. [PMID: 38973528 DOI: 10.1097/meg.0000000000002815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
INTRODUCTION The USA has the highest age-standardized prevalence of inflammatory bowel disease (IBD). Both genetic and environmental factors have been implicated in IBD flares and multiple strategies are centered around avoiding dietary triggers to maintain remission. Chat-based artificial intelligence (CB-AI) has shown great potential in enhancing patient education in medicine. We evaluate the role of CB-AI in patient education on dietary management of IBD. METHODS Six questions evaluating important concepts about the dietary management of IBD which then were posed to three CB-AI models - ChatGPT, BingChat, and YouChat three different times. All responses were graded for appropriateness and reliability by two physicians using dietary information from the Crohn's and Colitis Foundation. The responses were graded as reliably appropriate, reliably inappropriate, and unreliable. The expert assessment of the reviewing physicians was validated by the joint probability of agreement for two raters. RESULTS ChatGPT provided reliably appropriate responses to questions on dietary management of IBD more often than BingChat and YouChat. There were two questions that more than one CB-AI provided unreliable responses to. Each CB-AI provided examples within their responses, but the examples were not always appropriate. Whether the response was appropriate or not, CB-AIs mentioned consulting with an expert in the field. The inter-rater reliability was 88.9%. DISCUSSION CB-AIs have the potential to improve patient education and outcomes but studies evaluating their appropriateness for various health conditions are sparse. Our study showed that CB-AIs have the ability to provide appropriate answers to most questions regarding the dietary management of IBD.
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Affiliation(s)
- Haider A Naqvi
- Department of Medicine, Medstar Union Memorial Hospital
- Department of Medicine, Medstar Franklin Square Medical Center
| | - Thilini Delungahawatta
- Department of Medicine, Medstar Union Memorial Hospital
- Department of Medicine, Medstar Franklin Square Medical Center
| | - Joseph O Atarere
- Department of Medicine, Medstar Union Memorial Hospital
- Department of Medicine, Medstar Franklin Square Medical Center
| | | | - Jasmine B Barrow
- Department of Gastroenterology, Medstar Franklin Square Medical Center, Baltimore, Maryland
| | - Mark C Mattar
- Department of Gastroenterology, MedStar Georgetown University Hospital
- Department of Medicine, Georgetown University Medical Center, Washington, DC, USA
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Laymouna M, Ma Y, Lessard D, Schuster T, Engler K, Lebouché B. Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review. J Med Internet Res 2024; 26:e56930. [PMID: 39042446 PMCID: PMC11303905 DOI: 10.2196/56930] [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: 02/02/2024] [Revised: 04/07/2024] [Accepted: 04/12/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND Chatbots, or conversational agents, have emerged as significant tools in health care, driven by advancements in artificial intelligence and digital technology. These programs are designed to simulate human conversations, addressing various health care needs. However, no comprehensive synthesis of health care chatbots' roles, users, benefits, and limitations is available to inform future research and application in the field. OBJECTIVE This review aims to describe health care chatbots' characteristics, focusing on their diverse roles in the health care pathway, user groups, benefits, and limitations. METHODS A rapid review of published literature from 2017 to 2023 was performed with a search strategy developed in collaboration with a health sciences librarian and implemented in the MEDLINE and Embase databases. Primary research studies reporting on chatbot roles or benefits in health care were included. Two reviewers dual-screened the search results. Extracted data on chatbot roles, users, benefits, and limitations were subjected to content analysis. RESULTS The review categorized chatbot roles into 2 themes: delivery of remote health services, including patient support, care management, education, skills building, and health behavior promotion, and provision of administrative assistance to health care providers. User groups spanned across patients with chronic conditions as well as patients with cancer; individuals focused on lifestyle improvements; and various demographic groups such as women, families, and older adults. Professionals and students in health care also emerged as significant users, alongside groups seeking mental health support, behavioral change, and educational enhancement. The benefits of health care chatbots were also classified into 2 themes: improvement of health care quality and efficiency and cost-effectiveness in health care delivery. The identified limitations encompassed ethical challenges, medicolegal and safety concerns, technical difficulties, user experience issues, and societal and economic impacts. CONCLUSIONS Health care chatbots offer a wide spectrum of applications, potentially impacting various aspects of health care. While they are promising tools for improving health care efficiency and quality, their integration into the health care system must be approached with consideration of their limitations to ensure optimal, safe, and equitable use.
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Affiliation(s)
- Moustafa Laymouna
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC, Canada
| | - Yuanchao Ma
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC, Canada
- Chronic and Viral Illness Service, Division of Infectious Disease, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
- Department of Biomedical Engineering, Polytechnique Montréal, Montreal, QC, Canada
| | - David Lessard
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC, Canada
- Chronic and Viral Illness Service, Division of Infectious Disease, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
| | - Tibor Schuster
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
| | - Kim Engler
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC, Canada
- Chronic and Viral Illness Service, Division of Infectious Disease, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
| | - Bertrand Lebouché
- Department of Family Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada
- Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada
- Infectious Diseases and Immunity in Global Health Program, Research Institute of McGill University Health Centre, Montreal, QC, Canada
- Chronic and Viral Illness Service, Division of Infectious Disease, Department of Medicine, McGill University Health Centre, Montreal, QC, Canada
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Durant AM, Medero RC, Briggs LG, Choudry MM, Nguyen M, Channar A, Ghaffar U, Banerjee I, Bin Riaz I, Abdul-Muhsin H. The Current Application and Future Potential of Artificial Intelligence in Renal Cancer. Urology 2024:S0090-4295(24)00565-X. [PMID: 39029807 DOI: 10.1016/j.urology.2024.07.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/12/2024] [Accepted: 07/06/2024] [Indexed: 07/21/2024]
Abstract
Artificial intelligence (AI) is the integration of human tasks into machine processes. The role of AI in kidney cancer evaluation, management, and outcome predictions are constantly evolving. We performed a narrative review utilizing PubMed electronic database to query AI as a method of analysis in kidney cancer research. Key search-words included: Artificial Intelligence, Supervised/Unsupervised Machine Learning, Deep Learning, Natural Language Processing, Neural Networks, radiomics, pathomics, and kidney or renal neoplasms or cancer. 72 clinically relevant and impactful studies related to imaging, histopathology, and outcomes were recognized. We anticipate the incorporation of AI tools into future clinical decision-making for kidney cancer.
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Affiliation(s)
- Adri M Durant
- Department of Urology, Mayo Clinic Arizona, Phoenix, AZ.
| | - Ramon Correa Medero
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ
| | | | | | - Mimi Nguyen
- Department of Urology, Mayo Clinic Arizona, Phoenix, AZ
| | - Aneeta Channar
- Division of Hematology and Oncology, Department of Internal Medicine, Mayo Clinic Arizona, Phoenix, AZ
| | - Umar Ghaffar
- Department of Urology, Mayo Clinic Rochester, Rochester, MN
| | - Imon Banerjee
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ; Department of Radiology, Mayo Clinic Arizona, Scottsdale, AZ
| | - Irbaz Bin Riaz
- Division of Hematology and Oncology, Department of Internal Medicine, Mayo Clinic Arizona, Phoenix, AZ
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Musheyev D, Pan A, Gross P, Kamyab D, Kaplinsky P, Spivak M, Bragg MA, Loeb S, Kabarriti AE. Readability and Information Quality in Cancer Information From a Free vs Paid Chatbot. JAMA Netw Open 2024; 7:e2422275. [PMID: 39058491 PMCID: PMC11282443 DOI: 10.1001/jamanetworkopen.2024.22275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 05/15/2024] [Indexed: 07/28/2024] Open
Abstract
Importance The mainstream use of chatbots requires a thorough investigation of their readability and quality of information. Objective To identify readability and quality differences in information between a free and paywalled chatbot cancer-related responses, and to explore if more precise prompting can mitigate any observed differences. Design, Setting, and Participants This cross-sectional study compared readability and information quality of a chatbot's free vs paywalled responses with Google Trends' top 5 search queries associated with breast, lung, prostate, colorectal, and skin cancers from January 1, 2021, to January 1, 2023. Data were extracted from the search tracker, and responses were produced by free and paywalled ChatGPT. Data were analyzed from December 20, 2023, to January 15, 2024. Exposures Free vs paywalled chatbot outputs with and without prompt: "Explain the following at a sixth grade reading level: [nonprompted input]." Main Outcomes and Measures The primary outcome measured the readability of a chatbot's responses using Flesch Reading Ease scores (0 [graduate reading level] to 100 [easy fifth grade reading level]). Secondary outcomes included assessing consumer health information quality with the validated DISCERN instrument (overall score from 1 [low quality] to 5 [high quality]) for each response. Scores were compared between the 2 chatbot models with and without prompting. Results This study evaluated 100 chatbot responses. Nonprompted free chatbot responses had lower readability (median [IQR] Flesh Reading ease scores, 52.60 [44.54-61.46]) than nonprompted paywalled chatbot responses (62.48 [54.83-68.40]) (P < .05). However, prompting the free chatbot to reword responses at a sixth grade reading level was associated with increased reading ease scores than the paywalled chatbot nonprompted responses (median [IQR], 71.55 [68.20-78.99]) (P < .001). Prompting was associated with increases in reading ease in both free (median [IQR], 71.55 [68.20-78.99]; P < .001)and paywalled versions (median [IQR], 75.64 [70.53-81.12]; P < .001). There was no significant difference in overall DISCERN scores between the chatbot models, with and without prompting. Conclusions and Relevance In this cross-sectional study, paying for the chatbot was found to provide easier-to-read responses, but prompting the free version of the chatbot was associated with increased response readability without changing information quality. Educating the public on how to prompt chatbots may help promote equitable access to health information.
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Affiliation(s)
- David Musheyev
- Department of Urology, State University of New York Downstate Health Sciences University, New York
| | - Alexander Pan
- Department of Urology, State University of New York Downstate Health Sciences University, New York
| | - Preston Gross
- Department of Urology, State University of New York Downstate Health Sciences University, New York
| | - Daniel Kamyab
- Department of Urology, State University of New York Downstate Health Sciences University, New York
| | - Peter Kaplinsky
- Department of Urology, State University of New York Downstate Health Sciences University, New York
| | - Mark Spivak
- Department of Urology, State University of New York Downstate Health Sciences University, New York
| | - Marie A. Bragg
- Department of Urology, New York University and Manhattan Veterans Affairs, New York
- Marketing Department, Stern School of Business, New York University, New York
- Department of Population Health, New York University, New York
| | - Stacy Loeb
- Department of Urology, New York University and Manhattan Veterans Affairs, New York
- Department of Population Health, New York University, New York
| | - Abdo E. Kabarriti
- Department of Urology, State University of New York Downstate Health Sciences University, New York
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Sacca L, Lobaina D, Burgoa S, Lotharius K, Moothedan E, Gilmore N, Xie J, Mohler R, Scharf G, Knecht M, Kitsantas P. Promoting Artificial Intelligence for Global Breast Cancer Risk Prediction and Screening in Adult Women: A Scoping Review. J Clin Med 2024; 13:2525. [PMID: 38731054 PMCID: PMC11084581 DOI: 10.3390/jcm13092525] [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: 03/21/2024] [Revised: 04/01/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024] Open
Abstract
Background: Artificial intelligence (AI) algorithms can be applied in breast cancer risk prediction and prevention by using patient history, scans, imaging information, and analysis of specific genes for cancer classification to reduce overdiagnosis and overtreatment. This scoping review aimed to identify the barriers encountered in applying innovative AI techniques and models in developing breast cancer risk prediction scores and promoting screening behaviors among adult females. Findings may inform and guide future global recommendations for AI application in breast cancer prevention and care for female populations. Methods: The PRISMA-SCR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) was used as a reference checklist throughout this study. The Arksey and O'Malley methodology was used as a framework to guide this review. The framework methodology consisted of five steps: (1) Identify research questions; (2) Search for relevant studies; (3) Selection of studies relevant to the research questions; (4) Chart the data; (5) Collate, summarize, and report the results. Results: In the field of breast cancer risk detection and prevention, the following AI techniques and models have been applied: Machine and Deep Learning Model (ML-DL model) (n = 1), Academic Algorithms (n = 2), Breast Cancer Surveillance Consortium (BCSC), Clinical 5-Year Risk Prediction Model (n = 2), deep-learning computer vision AI algorithms (n = 2), AI-based thermal imaging solution (Thermalytix) (n = 1), RealRisks (n = 2), Breast Cancer Risk NAVIgation (n = 1), MammoRisk (ML-Based Tool) (n = 1), Various MLModels (n = 1), and various machine/deep learning, decision aids, and commercial algorithms (n = 7). In the 11 included studies, a total of 39 barriers to AI applications in breast cancer risk prediction and screening efforts were identified. The most common barriers in the application of innovative AI tools for breast cancer prediction and improved screening rates included lack of external validity and limited generalizability (n = 6), as AI was used in studies with either a small sample size or datasets with missing data. Many studies (n = 5) also encountered selection bias due to exclusion of certain populations based on characteristics such as race/ethnicity, family history, or past medical history. Several recommendations for future research should be considered. AI models need to include a broader spectrum and more complete predictive variables for risk assessment. Investigating long-term outcomes with improved follow-up periods is critical to assess the impacts of AI on clinical decisions beyond just the immediate outcomes. Utilizing AI to improve communication strategies at both a local and organizational level can assist in informed decision-making and compliance, especially in populations with limited literacy levels. Conclusions: The use of AI in patient education and as an adjunctive tool for providers is still early in its incorporation, and future research should explore the implementation of AI-driven resources to enhance understanding and decision-making regarding breast cancer screening, especially in vulnerable populations with limited literacy.
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Affiliation(s)
- Lea Sacca
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431, USA; (D.L.); (S.B.); (K.L.); (E.M.); (N.G.); (J.X.); (R.M.); (G.S.); (M.K.); (P.K.)
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Allen CG, Neil G, Halbert CH, Sterba KR, Nietert PJ, Welch B, Lenert L. Barriers and facilitators to the implementation of family cancer history collection tools in oncology clinical practices. J Am Med Inform Assoc 2024; 31:631-639. [PMID: 38164994 PMCID: PMC10873828 DOI: 10.1093/jamia/ocad243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 10/30/2023] [Accepted: 12/19/2023] [Indexed: 01/03/2024] Open
Abstract
INTRODUCTION This study aimed to identify barriers and facilitators to the implementation of family cancer history (FCH) collection tools in clinical practices and community settings by assessing clinicians' perceptions of implementing a chatbot interface to collect FCH information and provide personalized results to patients and providers. OBJECTIVES By identifying design and implementation features that facilitate tool adoption and integration into clinical workflows, this study can inform future FCH tool development and adoption in healthcare settings. MATERIALS AND METHODS Quantitative data were collected using survey to evaluate the implementation outcomes of acceptability, adoption, appropriateness, feasibility, and sustainability of the chatbot tool for collecting FCH. Semistructured interviews were conducted to gather qualitative data on respondents' experiences using the tool and recommendations for enhancements. RESULTS We completed data collection with 19 providers (n = 9, 47%), clinical staff (n = 5, 26%), administrators (n = 4, 21%), and other staff (n = 1, 5%) affiliated with the NCI Community Oncology Research Program. FCH was systematically collected using a wide range of tools at sites, with information being inserted into the patient's medical record. Participants found the chatbot tool to be highly acceptable, with the tool aligning with existing workflows, and were open to adopting the tool into their practice. DISCUSSION AND CONCLUSIONS We further the evidence base about the appropriateness of scripted chatbots to support FCH collection. Although the tool had strong support, the varying clinical workflows across clinic sites necessitate that future FCH tool development accommodates customizable implementation strategies. Implementation support is necessary to overcome technical and logistical barriers to enhance the uptake of FCH tools in clinical practices and community settings.
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Affiliation(s)
- Caitlin G Allen
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Grace Neil
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Chanita Hughes Halbert
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, United States
| | - Katherine R Sterba
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Paul J Nietert
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Brandon Welch
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
| | - Leslie Lenert
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States
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Thibaut G, Dabbagh A, Liverneaux P. Does Google's Bard Chatbot perform better than ChatGPT on the European hand surgery exam? INTERNATIONAL ORTHOPAEDICS 2024; 48:151-158. [PMID: 37968408 DOI: 10.1007/s00264-023-06034-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/01/2023] [Indexed: 11/17/2023]
Abstract
PURPOSE According to a previous research, the chatbot ChatGPT® V3.5 was unable to pass the first part of the European Board of Hand Surgery (EBHS) diploma examination. This study aimed to investigate whether Google's chatbot Bard® would have superior performance compared to ChatGPT on the EBHS diploma examination. METHODS Chatbots were asked to answer 18 EBHS multiple choice questions (MCQs) published in the Journal of Hand Surgery (European Volume) in five trials (A1 to A5). After A3, chatbots received correct answers, and after A4, incorrect answers. Consequently, their ability to modify their response was measured and compared. RESULTS Bard® scored 3/18 (A1), 1/18 (A2), 4/18 (A3) and 2/18 (A4 and A5). The average percentage of correct answers was 61.1% for A1, 62.2% for A2, 64.4% for A3, 65.6% for A4, 63.3% for A5 and 63.3% for all trials combined. Agreement was moderate from A1 to A5 (kappa = 0.62 (IC95% = [0.51; 0.73])) as well as from A1 to A3 (kappa = 0.60 (IC95% = [0.47; 0.74])). The formulation of Bard® responses was homogeneous, but its learning capacity is still developing. CONCLUSIONS The main hypothesis of our study was not proved since Bard did not score significantly higher than ChatGPT when answering the MCQs of the EBHS diploma exam. In conclusion, neither ChatGPT® nor Bard®, in their current versions, can pass the first part of the EBHS diploma exam.
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Affiliation(s)
- Goetsch Thibaut
- Department of Public Health, Strasbourg University Hospital, FMTS, GMRC, 1 avenue de l'hôpital, 67000, Strasbourg cedex, France
| | | | - Philippe Liverneaux
- ICube CNRS UMR7357, Strasbourg University, 2-4 rue Boussingault, 67000, Strasbourg, France.
- Department of Hand Surgery, Strasbourg University Hospitals, FMTS, 1 avenue Molière, 67200, Strasbourg, France.
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Mahesh Batra A, Reche A. A New Era of Dental Care: Harnessing Artificial Intelligence for Better Diagnosis and Treatment. Cureus 2023; 15:e49319. [PMID: 38143639 PMCID: PMC10748804 DOI: 10.7759/cureus.49319] [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: 11/09/2023] [Accepted: 11/23/2023] [Indexed: 12/26/2023] Open
Abstract
The integration of artificial intelligence (AI) into dental care holds the promise of revolutionizing the field by enhancing the accuracy of dental diagnosis and treatment. This paper explores the impact of AI in dental care, with a focus on its applications in diagnosis, treatment planning, and patient engagement. AI-driven dental imaging and radiography, computer-aided detection and diagnosis of dental conditions, and early disease detection and prevention are discussed in detail. Moreover, the paper delves into how AI assists in personalized treatment planning and provides predictive analytics for dental care. Ethical and privacy considerations, including data security, fairness, and regulatory aspects, are addressed, highlighting the need for a responsible and transparent approach to AI implementation. Finally, the paper underscores the potential for a collaborative partnership between AI and dental professionals to offer the best possible care to patients, making dental care more efficient, patient-centric, and effective. The advent of AI in dentistry presents a remarkable opportunity to improve oral health outcomes, benefiting both patients and the healthcare community.
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Affiliation(s)
- Aastha Mahesh Batra
- Dentistry, Sharad Pawar Dental College and Hospital, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Amit Reche
- Public Health Dentistry, Sharad Pawar Dental College and Hospital, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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