1
|
Chen D, Parsa R, Hope A, Hannon B, Mak E, Eng L, Liu FF, Fallah-Rad N, Heesters AM, Raman S. Physician and Artificial Intelligence Chatbot Responses to Cancer Questions From Social Media. JAMA Oncol 2024; 10:956-960. [PMID: 38753317 PMCID: PMC11099835 DOI: 10.1001/jamaoncol.2024.0836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/06/2023] [Indexed: 05/19/2024]
Abstract
Importance Artificial intelligence (AI) chatbots pose the opportunity to draft template responses to patient questions. However, the ability of chatbots to generate responses based on domain-specific knowledge of cancer remains to be tested. Objective To evaluate the competency of AI chatbots (GPT-3.5 [chatbot 1], GPT-4 [chatbot 2], and Claude AI [chatbot 3]) to generate high-quality, empathetic, and readable responses to patient questions about cancer. Design, Setting, and Participants This equivalence study compared the AI chatbot responses and responses by 6 verified oncologists to 200 patient questions about cancer from a public online forum. Data were collected on May 31, 2023. Exposures Random sample of 200 patient questions related to cancer from a public online forum (Reddit r/AskDocs) spanning from January 1, 2018, to May 31, 2023, was posed to 3 AI chatbots. Main Outcomes and Measures The primary outcomes were pilot ratings of the quality, empathy, and readability on a Likert scale from 1 (very poor) to 5 (very good). Two teams of attending oncology specialists evaluated each response based on pilot measures of quality, empathy, and readability in triplicate. The secondary outcome was readability assessed using Flesch-Kincaid Grade Level. Results Responses to 200 questions generated by chatbot 3, the best-performing AI chatbot, were rated consistently higher in overall measures of quality (mean, 3.56 [95% CI, 3.48-3.63] vs 3.00 [95% CI, 2.91-3.09]; P < .001), empathy (mean, 3.62 [95% CI, 3.53-3.70] vs 2.43 [95% CI, 2.32-2.53]; P < .001), and readability (mean, 3.79 [95% CI, 3.72-3.87] vs 3.07 [95% CI, 3.00-3.15]; P < .001) compared with physician responses. The mean Flesch-Kincaid Grade Level of physician responses (mean, 10.11 [95% CI, 9.21-11.03]) was not significantly different from chatbot 3 responses (mean, 10.31 [95% CI, 9.89-10.72]; P > .99) but was lower than those from chatbot 1 (mean, 12.33 [95% CI, 11.84-12.83]; P < .001) and chatbot 2 (mean, 11.32 [95% CI, 11.05-11.79]; P = .01). Conclusions and Relevance The findings of this study suggest that chatbots can generate quality, empathetic, and readable responses to patient questions comparable to physician responses sourced from an online forum. Further research is required to assess the scope, process integration, and patient and physician outcomes of chatbot-facilitated interactions.
Collapse
Affiliation(s)
- David Chen
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Ontario, Canada
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Rod Parsa
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Ontario, Canada
- Michael G. DeGroote School of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Andrew Hope
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Breffni Hannon
- Department of Supportive Care, University Health Network, Toronto, Ontario, Canada
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ernie Mak
- Department of Supportive Care, University Health Network, Toronto, Ontario, Canada
- Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Lawson Eng
- Division of Medical Oncology and Hematology, Department of Medicine, Princess Margaret Cancer Centre/University Health Network Toronto, Toronto, Ontario, Canada
- Division of Medical Oncology, Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Fei-Fei Liu
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Nazanin Fallah-Rad
- Division of Medical Oncology and Hematology, Department of Medicine, Princess Margaret Cancer Centre/University Health Network Toronto, Toronto, Ontario, Canada
| | - Ann M. Heesters
- Department of Clinical and Organizational Ethics, University Health Network, Toronto, Ontario, Canada
- The Institute for Education Research, University Health Network, Toronto, Ontario, Canada
- Dalla Lana School of Public Health and Joint Centre for Bioethics, University of Toronto, Toronto, Ontario, Canada
| | - Srinivas Raman
- Princess Margaret Hospital Cancer Centre, Radiation Medicine Program, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
2
|
Didier AJ, Moss G, Sutton JM. Applications of Artificial Intelligence for Cancer Patient Education. JOURNAL OF CANCER EDUCATION : THE OFFICIAL JOURNAL OF THE AMERICAN ASSOCIATION FOR CANCER EDUCATION 2024:10.1007/s13187-024-02471-4. [PMID: 38922554 DOI: 10.1007/s13187-024-02471-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/20/2024] [Indexed: 06/27/2024]
Affiliation(s)
- Alexander J Didier
- Department of Medicine, The University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA.
| | - Gabriel Moss
- Department of Medicine, The University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA
| | - Jeffrey M Sutton
- Division of Oncologic and Endocrine Surgery, Department of Surgery, Medical University of South Carolina, Charleston, SC, USA
| |
Collapse
|
3
|
Zhu L, Rong Y, McGee LA, Rwigema JCM, Patel SH. Testing and Validation of a Custom Retrained Large Language Model for the Supportive Care of HN Patients with External Knowledge Base. Cancers (Basel) 2024; 16:2311. [PMID: 39001375 PMCID: PMC11240646 DOI: 10.3390/cancers16132311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 07/16/2024] Open
Abstract
PURPOSE This study aimed to develop a retrained large language model (LLM) tailored to the needs of HN cancer patients treated with radiotherapy, with emphasis on symptom management and survivorship care. METHODS A comprehensive external database was curated for training ChatGPT-4, integrating expert-identified consensus guidelines on supportive care for HN patients and correspondences from physicians and nurses within our institution's electronic medical records for 90 HN patients. The performance of our model was evaluated using 20 patient post-treatment inquiries that were then assessed by three Board certified radiation oncologists (RadOncs). The rating of the model was assessed on a scale of 1 (strongly disagree) to 5 (strongly agree) based on accuracy, clarity of response, completeness s, and relevance. RESULTS The average scores for the 20 tested questions were 4.25 for accuracy, 4.35 for clarity, 4.22 for completeness, and 4.32 for relevance, on a 5-point scale. Overall, 91.67% (220 out of 240) of assessments received scores of 3 or higher, and 83.33% (200 out of 240) received scores of 4 or higher. CONCLUSION The custom-trained model demonstrates high accuracy in providing support to HN patients offering evidence-based information and guidance on their symptom management and survivorship care.
Collapse
Affiliation(s)
| | - Yi Rong
- Correspondence: (Y.R.); (S.H.P.)
| | | | | | - Samir H. Patel
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA; (L.Z.); (L.A.M.); (J.-C.M.R.)
| |
Collapse
|
4
|
Liu L, Qu S, Zhao H, Kong L, Xie Z, Jiang Z, Zou P. Global trends and hotspots of ChatGPT in medical research: a bibliometric and visualized study. Front Med (Lausanne) 2024; 11:1406842. [PMID: 38818395 PMCID: PMC11137200 DOI: 10.3389/fmed.2024.1406842] [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: 03/25/2024] [Accepted: 05/06/2024] [Indexed: 06/01/2024] Open
Abstract
Objective With the rapid advancement of Chat Generative Pre-Trained Transformer (ChatGPT) in medical research, our study aimed to identify global trends and focal points in this domain. Method All publications on ChatGPT in medical research were retrieved from the Web of Science Core Collection (WoSCC) by Clarivate Analytics from January 1, 2023, to January 31, 2024. The research trends and focal points were visualized and analyzed using VOSviewer and CiteSpace. Results A total of 1,239 publications were collected and analyzed. The USA contributed the largest number of publications (458, 37.145%) with the highest total citation frequencies (2,461) and the largest H-index. Harvard University contributed the highest number of publications (33) among all full-time institutions. The Cureus Journal of Medical Science published the most ChatGPT-related research (127, 10.30%). Additionally, Wiwanitkit V contributed the majority of publications in this field (20). "Artificial Intelligence (AI) and Machine Learning (ML)," "Education and Training," "Healthcare Applications," and "Data Analysis and Technology" emerged as the primary clusters of keywords. These areas are predicted to remain hotspots in future research in this field. Conclusion Overall, this study signifies the interdisciplinary nature of ChatGPT research in medicine, encompassing AI and ML technologies, education and training initiatives, diverse healthcare applications, and data analysis and technology advancements. These areas are expected to remain at the forefront of future research, driving continued innovation and progress in the field of ChatGPT in medical research.
Collapse
Affiliation(s)
- Ling Liu
- Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People’s Hospital of Guangxi Zhuang Autonomous Region), Guilin, China
- School of Integrated Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Shenhong Qu
- Department of Otolaryngology-Head and Neck Oncology, The People’s Hospital of Guangxi Zhuang Autonoms Region, Nanning, China
| | - Haiyun Zhao
- Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People’s Hospital of Guangxi Zhuang Autonomous Region), Guilin, China
| | - Lingping Kong
- Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People’s Hospital of Guangxi Zhuang Autonomous Region), Guilin, China
| | - Zhuzhu Xie
- School of Integrated Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Zhichao Jiang
- Hunan Provincial Brain Hospital, The Second People’s Hospital of Hunan Province, Changsha, China
| | - Pan Zou
- Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People’s Hospital of Guangxi Zhuang Autonomous Region), Guilin, China
| |
Collapse
|
5
|
Yılmaz IBE, Doğan L. Talking technology: exploring chatbots as a tool for cataract patient education. Clin Exp Optom 2024:1-9. [PMID: 38194585 DOI: 10.1080/08164622.2023.2298812] [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: 09/02/2023] [Accepted: 12/15/2023] [Indexed: 01/11/2024] Open
Abstract
CLINICAL RELEVANCE Worldwide, millions suffer from cataracts, which impair vision and quality of life. Cataract education improves outcomes, satisfaction, and treatment adherence. Lack of health literacy, language and cultural barriers, personal preferences, and limited resources may all impede effective communication. BACKGROUND AI can improve patient education by providing personalised, interactive, and accessible information tailored to patient understanding, interest, and motivation. AI chatbots can have human-like conversations and give advice on numerous topics. METHODS This study investigated the efficacy of chatbots in cataract patient education relative to traditional resources like the AAO website, focusing on information accuracy,understandability, actionability, and readability. A descriptive comparative design was used to analyse quantitative data from frequently asked questions about cataracts answered by ChatGPT, Bard, Bing AI, and the AAO website. SOLO taxonomy, PEMAT, and the Flesch-Kincaid ease score were used to collect and analyse the data. RESULTS Chatbots scored higher than AAO website on cataract-related questions in terms of accuracy (mean SOLO score ChatGPT: 3.1 ± 0.31, Bard: 2.9 ± 0.72, Bing AI: 2.65 ± 0.49, AAO website: 2.4 ± 0.6, (p < 0.001)). For understandability (mean PEMAT-U score AAO website: 0,89 ± 0,04, ChatGPT 0,84 ± 0,02, Bard: 0,84 ± 0,02, Bing AI: 0,81 ± 0,02, (p < 0.001)), and actionability (mean PEMAT-A score ChatGPT: 0.86 ± 0.03, Bard: 0.85 ± 0.06, Bing AI: 0.81 ± 0.05, AAO website: 0.81 ± 0.06, (p < 0.001)) AAO website scored better than chatbots. Flesch-Kincaid readability ease analysis showed that Bard (55,5 ± 8,48) had the highest mean score, followed by AAO website (51,96 ± 12,46), Bing AI (41,77 ± 9,53), and ChatGPT (34,38 ± 9,75, (p < 0.001)). CONCLUSION Chatbots have the potential to provide more detailed and accurate data than the AAO website. On the other hand, the AAO website has the advantage of providing information that is more understandable and practical. When patient preferences are not taken into account, generalised or biased information can decrease reliability.
Collapse
Affiliation(s)
| | - Levent Doğan
- Ophthalmology Department, Kilis State Hospital, Kilis, Turkey
| |
Collapse
|
6
|
Talyshinskii A, Naik N, Hameed BMZ, Juliebø-Jones P, Somani BK. Potential of AI-Driven Chatbots in Urology: Revolutionizing Patient Care Through Artificial Intelligence. Curr Urol Rep 2024; 25:9-18. [PMID: 37723300 PMCID: PMC10787686 DOI: 10.1007/s11934-023-01184-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/05/2023] [Indexed: 09/20/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) chatbots have emerged as a potential tool to transform urology by improving patient care and physician efficiency. With an emphasis on their potential advantages and drawbacks, this literature review offers a thorough assessment of the state of AI-driven chatbots in urology today. RECENT FINDINGS The capacity of AI-driven chatbots in urology to give patients individualized and timely medical advice is one of its key advantages. Chatbots can help patients prioritize their symptoms and give advice on the best course of treatment. By automating administrative duties and offering clinical decision support, chatbots can also help healthcare providers. Before chatbots are widely used in urology, there are a few issues that need to be resolved. The precision of chatbot diagnoses and recommendations might be impacted by technical constraints like system errors and flaws. Additionally, issues regarding the security and privacy of patient data must be resolved, and chatbots must adhere to all applicable laws. Important issues that must be addressed include accuracy and dependability because any mistakes or inaccuracies could seriously harm patients. The final obstacle is resistance from patients and healthcare professionals who are hesitant to use new technology or who value in-person encounters. AI-driven chatbots have the potential to significantly improve urology care and efficiency. However, it is essential to thoroughly test and ensure the accuracy of chatbots, address privacy and security concerns, and design user-friendly chatbots that can integrate into existing workflows. By exploring various scenarios and examining the current literature, this review provides an analysis of the prospects and limitations of implementing chatbots in urology.
Collapse
Affiliation(s)
- Ali Talyshinskii
- Department of Urology, Astana Medical University, Astana, Kazakhstan
| | - Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - B M Zeeshan Hameed
- Department of Urology, Father Muller Medical College, Mangalore, Karnataka, India
| | - Patrick Juliebø-Jones
- Department of Urology, Haukeland University Hospital, Bergen, Norway.
- Department of Clinical Medicine, University of Bergen, Bergen, Norway.
| | | |
Collapse
|
7
|
Melhem SJ, Kayyali R. Multilayer framework for digital multicomponent platform design for colorectal survivors and carers: a qualitative study. Front Public Health 2023; 11:1272344. [PMID: 38115846 PMCID: PMC10728820 DOI: 10.3389/fpubh.2023.1272344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 11/08/2023] [Indexed: 12/21/2023] Open
Abstract
Background The advent of eHealth services offers the potential to support colorectal cancer (CRC) survivors and their informal caregivers (ICs), yet research into user needs and design requirements remains scant. This exploratory qualitative study addresses this knowledge gap by focusing on the development of a Digital Multicomponent Platform (DMP) designed to provide comprehensive support to these populations. Aims The objective of this research is to use qualitative methodologies to identify key user needs and design requirements for eHealth services. It seeks to propose and apply a multi-tiered framework for creating a DMP that encapsulates the needs of CRC survivors and their ICs. Methods Skype-based focus groups (FGs) were utilized to gather qualitative data from CRC survivors and ICs. This approach served to elicit crucial themes integral to the design of the DMP. A multi-tiered framework was subsequently developed to integrate user-centered design (UCD) principles and requirements with predetermined outcomes, eHealth services, and IT infrastructure. Results The first stage of the analysis identified five crucial themes: (1) the importance of healthcare system interaction via eHealth, (2) interaction between healthcare providers and peers, (3) lifestyle and wellness considerations, (4) platform content and user interface requirements, (5) caregiver support. The second stage analysis applied the multi-tiered framework, to determine the DMP that was conceptualized from these themes, underscores the significance of personalized content, caregiver involvement, and integration with electronic health records (EHRs). Conclusion The study offers novel insights into the design and development of digital supportive care interventions for CRC survivors and their caregivers. The results highlight the utility of user-centered design principles, the significance of personalized content and caregiver involvement, and the need for a unified health data platform that promotes communication among patients, healthcare providers, and peers. This multi-tiered framework could serve as a prototype for future eHealth service designs.
Collapse
Affiliation(s)
- Samar J. Melhem
- Department of Pharmacy, School of Life Sciences, Pharmacy and Chemistry, Kingston University London, Kingston upon Thames, Surrey, United Kingdom
| | | |
Collapse
|
8
|
Alanzi TM, Alzahrani W, Albalawi NS, Allahyani T, Alghamdi A, Al-Zahrani H, Almutairi A, Alzahrani H, Almulhem L, Alanzi N, Al Moarfeg A, Farhah N. Public Awareness of Obesity as a Risk Factor for Cancer in Central Saudi Arabia: Feasibility of ChatGPT as an Educational Intervention. Cureus 2023; 15:e50781. [PMID: 38239542 PMCID: PMC10795720 DOI: 10.7759/cureus.50781] [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] [Accepted: 12/17/2023] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND While the link between obesity and chronic diseases such as diabetes and cardiovascular disorders is well-documented, there is a growing body of evidence connecting obesity with an increased risk of cancer. However, public awareness of this connection remains limited. STUDY PURPOSE To analyze public awareness of overweight/obesity as a risk factor for cancer and analyze public perceptions on the feasibility of ChatGPT, an artificial intelligence-based conversational agent, as an educational intervention tool. METHODS A mixed-methods approach including deductive quantitative cross-sectional approach to draw precise conclusions based on empirical evidence on public awareness of the link between obesity and cancer; and inductive qualitative approach to interpret public perceptions on using ChatGPT for creating awareness of obesity, cancer and its risk factors was used in this study. Participants included adult residents in Saudi Arabia. A total of 486 individuals and 21 individuals were included in the survey and semi-structured interviews respectively. RESULTS About 65% of the participants are not completely aware of cancer and its risk factors. Significant differences in awareness were observed concerning age groups (p < .0001), socio-economic status (p = .041), and regional distribution (p = .0351). A total of 10 themes were analyzed from the interview data, which included four positive factors (accessibility, personalization, cost-effectiveness, anonymity and privacy, multi-language support) and five negative factors (information inaccuracy, lack of emotional intelligence, dependency and overreliance, data privacy and security, and inability to provide physical support or diagnosis). CONCLUSION This study has underscored the potential of leveraging ChatGPT as a valuable public awareness tool for cancer in Saudi Arabia.
Collapse
Affiliation(s)
- Turki M Alanzi
- Department of Health Information Management and Technology, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Wala Alzahrani
- Department of Clinical Nutrition, College of Applied Medical Sciences, King Abdulaziz University, Jeddah, SAU
| | | | - Taif Allahyani
- College of Applied Medical Sciences, Umm Al-Qura University, Makkah, SAU
| | | | - Haneen Al-Zahrani
- Department of Hematology, Armed Forces Hospital at King Abdulaziz Airbase Dhahran, Dhahran, SAU
| | - Awatif Almutairi
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Jouf University, Jouf, SAU
| | | | | | - Nouf Alanzi
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Jouf University, Jouf, SAU
| | | | - Nesren Farhah
- Department of Health Informatics, College of Health Sciences, Saudi Electronic University, Riyadh, SAU
| |
Collapse
|
9
|
Singh A, Randive S, Breggia A, Ahmad B, Christman R, Amal S. Enhancing Prostate Cancer Diagnosis with a Novel Artificial Intelligence-Based Web Application: Synergizing Deep Learning Models, Multimodal Data, and Insights from Usability Study with Pathologists. Cancers (Basel) 2023; 15:5659. [PMID: 38067363 PMCID: PMC10705310 DOI: 10.3390/cancers15235659] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/22/2023] [Accepted: 11/23/2023] [Indexed: 05/29/2024] Open
Abstract
Prostate cancer remains a significant cause of male cancer mortality in the United States, with an estimated 288,300 new cases in 2023. Accurate grading of prostate cancer is crucial for ascertaining disease severity and shaping treatment strategies. Modern deep learning techniques show promise in grading biopsies, but there is a gap in integrating these advances into clinical practice. Our web platform tackles this challenge by integrating human expertise with AI-driven grading, incorporating diverse data sources. We gathered feedback from four pathologists and one medical practitioner to assess usability and real-world alignment through a survey and the NASA TLX Usability Test. Notably, 60% of users found it easy to navigate, rating it 5.5 out of 7 for ease of understanding. Users appreciated self-explanatory information in popup tabs. For ease of use, all users favored the detailed summary tab, rating it 6.5 out of 7. While 80% felt patient demographics beyond age were unnecessary, high-resolution biopsy images were deemed vital. Acceptability was high, with all users willing to adopt the app, and some believed it could reduce workload. The NASA TLX Usability Test indicated a low-moderate perceived workload, suggesting room for improved explanations and data visualization.
Collapse
Affiliation(s)
- Akarsh Singh
- College of Engineering, Northeastern University, Boston, MA 02115, USA; (A.S.); (S.R.)
| | - Shruti Randive
- College of Engineering, Northeastern University, Boston, MA 02115, USA; (A.S.); (S.R.)
| | - Anne Breggia
- Maine Health Institute for Research, Scarborough, ME 04074, USA
| | - Bilal Ahmad
- Maine Medical Center, Portland, ME 04102, USA; (B.A.); (R.C.)
| | | | - Saeed Amal
- The Roux Institute, Department of Bioengineering, College of Engineering, Northeastern University, Boston, MA 02115, USA
| |
Collapse
|
10
|
Song H, Xia Y, Luo Z, Liu H, Song Y, Zeng X, Li T, Zhong G, Li J, Chen M, Zhang G, Xiao B. Evaluating the Performance of Different Large Language Models on Health Consultation and Patient Education in Urolithiasis. J Med Syst 2023; 47:125. [PMID: 37999899 DOI: 10.1007/s10916-023-02021-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 11/14/2023] [Indexed: 11/25/2023]
Abstract
OBJECTIVES To evaluate the effectiveness of four large language models (LLMs) (Claude, Bard, ChatGPT4, and New Bing) that have large user bases and significant social attention, in the context of medical consultation and patient education in urolithiasis. MATERIALS AND METHODS In this study, we developed a questionnaire consisting of 21 questions and 2 clinical scenarios related to urolithiasis. Subsequently, clinical consultations were simulated for each of the four models to assess their responses to the questions. Urolithiasis experts then evaluated the model responses in terms of accuracy, comprehensiveness, ease of understanding, human care, and clinical case analysis ability based on a predesigned 5-point Likert scale. Visualization and statistical analyses were then employed to compare the four models and evaluate their performance. RESULTS All models yielded satisfying performance, except for Bard, who failed to provide a valid response to Question 13. Claude consistently scored the highest in all dimensions compared with the other three models. ChatGPT4 ranked second in accuracy, with a relatively stable output across multiple tests, but shortcomings were observed in empathy and human caring. Bard exhibited the lowest accuracy and overall performance. Claude and ChatGPT4 both had a high capacity to analyze clinical cases of urolithiasis. Overall, Claude emerged as the best performer in urolithiasis consultations and education. CONCLUSION Claude demonstrated superior performance compared with the other three in urolithiasis consultation and education. This study highlights the remarkable potential of LLMs in medical health consultations and patient education, although professional review, further evaluation, and modifications are still required.
Collapse
Affiliation(s)
- Haifeng Song
- Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China
- Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 102218, China
| | - Yi Xia
- Department of Urology, Zhongda Hospital, Southeast University, 87 Dingjiaqiao, Nanjing, 210009, China
- School of Medicine, Southeast University, Nanjing, 210009, China
| | - Zhichao Luo
- Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China
- Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 102218, China
| | - Hui Liu
- Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China
- Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 102218, China
| | - Yan Song
- Department of Urology, Sheng Jing Hospital of China Medical University, Shenyang, 110000, China
| | - Xue Zeng
- Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China
- Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 102218, China
| | - Tianjie Li
- Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China
- Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 102218, China
| | - Guangxin Zhong
- Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China
- Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 102218, China
| | - Jianxing Li
- Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China
- Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 102218, China
| | - Ming Chen
- Department of Urology, Zhongda Hospital, Southeast University, 87 Dingjiaqiao, Nanjing, 210009, China
| | - Guangyuan Zhang
- Department of Urology, Zhongda Hospital, Southeast University, 87 Dingjiaqiao, Nanjing, 210009, China.
| | - Bo Xiao
- Department of Urology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, 168 Litang Rd, Beijing, 102218, China.
- Institute of Urology, School of Clinical Medicine, Tsinghua University, Beijing, 102218, China.
| |
Collapse
|
11
|
Wong RSY, Ming LC, Raja Ali RA. The Intersection of ChatGPT, Clinical Medicine, and Medical Education. JMIR MEDICAL EDUCATION 2023; 9:e47274. [PMID: 37988149 DOI: 10.2196/47274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 06/16/2023] [Accepted: 06/30/2023] [Indexed: 11/22/2023]
Abstract
As we progress deeper into the digital age, the robust development and application of advanced artificial intelligence (AI) technology, specifically generative language models like ChatGPT (OpenAI), have potential implications in all sectors including medicine. This viewpoint article aims to present the authors' perspective on the integration of AI models such as ChatGPT in clinical medicine and medical education. The unprecedented capacity of ChatGPT to generate human-like responses, refined through Reinforcement Learning with Human Feedback, could significantly reshape the pedagogical methodologies within medical education. Through a comprehensive review and the authors' personal experiences, this viewpoint article elucidates the pros, cons, and ethical considerations of using ChatGPT within clinical medicine and notably, its implications for medical education. This exploration is crucial in a transformative era where AI could potentially augment human capability in the process of knowledge creation and dissemination, potentially revolutionizing medical education and clinical practice. The importance of maintaining academic integrity and professional standards is highlighted. The relevance of establishing clear guidelines for the responsible and ethical use of AI technologies in clinical medicine and medical education is also emphasized.
Collapse
Affiliation(s)
- Rebecca Shin-Yee Wong
- Department of Medical Education, School of Medical and Life Sciences, Sunway University, Selangor, Malaysia
- Faculty of Medicine, Nursing and Health Sciences, SEGi University, Petaling Jaya, Malaysia
| | - Long Chiau Ming
- School of Medical and Life Sciences, Sunway University, Selangor, Malaysia
| | - Raja Affendi Raja Ali
- School of Medical and Life Sciences, Sunway University, Selangor, Malaysia
- GUT Research Group, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| |
Collapse
|
12
|
Yang J. ChatGPTs' Journey in Medical Revolution: A Potential Panacea or a Hidden Pathogen? Ann Biomed Eng 2023; 51:2356-2358. [PMID: 37273063 DOI: 10.1007/s10439-023-03264-4] [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: 05/29/2023] [Accepted: 05/31/2023] [Indexed: 06/06/2023]
Abstract
At the fascinating intersection of artificial intelligence and medicine, ChatGPT morphs into a compact, personal digital physician. With a simple click, it furnishes an abundance of health-related information, initial medical consultations, and a plethora of disease management recommendations. Moreover, it stands at the ready to provide immediate mental health assistance in times of psychological distress. Yet, each innovation carries inherent challenges. As we embrace the conveniences proffered by ChatGPT, it is imperative that we grapple with associated issues such as data privacy, risk of misdiagnosis, complexities in human-machine interaction, and particular situations that elude its understanding. Let's probe further into this intriguing world, brimming with contention and prospects, and observe how ChatGPT traverses the landscape of digital health, uncovering the potential it holds for the future evolution of medical practice.
Collapse
Affiliation(s)
- Jing Yang
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
| |
Collapse
|
13
|
Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, Aldairem A, Alrashed M, Bin Saleh K, Badreldin HA, Al Yami MS, Al Harbi S, Albekairy AM. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC MEDICAL EDUCATION 2023; 23:689. [PMID: 37740191 PMCID: PMC10517477 DOI: 10.1186/s12909-023-04698-z] [Citation(s) in RCA: 136] [Impact Index Per Article: 136.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 09/19/2023] [Indexed: 09/24/2023]
Abstract
INTRODUCTION Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools. RESEARCH SIGNIFICANCE This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies. MATERIALS AND METHODS The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application. RESULTS Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust. CONCLUSION AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.
Collapse
Affiliation(s)
- Shuroug A Alowais
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia.
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia.
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia.
| | - Sahar S Alghamdi
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Nada Alsuhebany
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Tariq Alqahtani
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
- Department of Pharmaceutical Sciences, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Abdulrahman I Alshaya
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Sumaya N Almohareb
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Atheer Aldairem
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Mohammed Alrashed
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Khalid Bin Saleh
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Hisham A Badreldin
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Majed S Al Yami
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Shmeylan Al Harbi
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Abdulkareem M Albekairy
- Department of Pharmacy Practice, College of Pharmacy, King Saud bin Abdulaziz University for Health Sciences, Prince Mutib Ibn Abdullah Ibn Abdulaziz Rd, Riyadh, 14611, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Pharmaceutical Care Department, King Abdulaziz Medical City, National Guard Health Affairs, Riyadh, Saudi Arabia
| |
Collapse
|
14
|
Kassab J, Nasr L, Gebrael G, Chedid El Helou M, Saba L, Haroun E, Dahdah JE, Nasr F. AI-based online chat and the future of oncology care: a promising technology or a solution in search of a problem? Front Oncol 2023; 13:1176617. [PMID: 37305580 PMCID: PMC10250003 DOI: 10.3389/fonc.2023.1176617] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/16/2023] [Indexed: 06/13/2023] Open
Affiliation(s)
- Joseph Kassab
- Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Lewis Nasr
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Georges Gebrael
- Department of Hematology and Oncology, Saint-Joseph University of Beirut, Beirut, Lebanon
| | | | - Ludovic Saba
- Maroone Cancer Center, Cleveland Clinic Florida, Weston, FL, United States
| | - Elio Haroun
- Division of Hematology and Oncology, State University of New York Upstate Medical University, Syracuse, NY, United States
| | - Joseph El Dahdah
- Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Fadi Nasr
- Department of Hematology and Oncology, Saint-Joseph University of Beirut, Beirut, Lebanon
| |
Collapse
|