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Vaishya R, Iyengar KP, Patralekh MK, Botchu R, Shirodkar K, Jain VK, Vaish A, Scarlat MM. Effectiveness of AI-powered Chatbots in responding to orthopaedic postgraduate exam questions-an observational study. INTERNATIONAL ORTHOPAEDICS 2024; 48:1963-1969. [PMID: 38619565 DOI: 10.1007/s00264-024-06182-9] [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: 03/25/2024] [Accepted: 04/03/2024] [Indexed: 04/16/2024]
Abstract
PURPOSE This study analyses the performance and proficiency of the three Artificial Intelligence (AI) generative chatbots (ChatGPT-3.5, ChatGPT-4.0, Bard Google AI®) and in answering the Multiple Choice Questions (MCQs) of postgraduate (PG) level orthopaedic qualifying examinations. METHODS A series of 120 mock Single Best Answer' (SBA) MCQs with four possible options named A, B, C and D as answers on various musculoskeletal (MSK) conditions covering Trauma and Orthopaedic curricula were compiled. A standardised text prompt was used to generate and feed ChatGPT (both 3.5 and 4.0 versions) and Google Bard programs, which were then statistically analysed. RESULTS Significant differences were found between responses from Chat GPT 3.5 with Chat GPT 4.0 (Chi square = 27.2, P < 0.001) and on comparing both Chat GPT 3.5 (Chi square = 63.852, P < 0.001) with Chat GPT 4.0 (Chi square = 44.246, P < 0.001) with. Bard Google AI® had 100% efficiency and was significantly more efficient than both Chat GPT 3.5 with Chat GPT 4.0 (p < 0.0001). CONCLUSION The results demonstrate the variable potential of the different AI generative chatbots (Chat GPT 3.5, Chat GPT 4.0 and Bard Google) in their ability to answer the MCQ of PG-level orthopaedic qualifying examinations. Bard Google AI® has shown superior performance than both ChatGPT versions, underlining the potential of such large language processing models in processing and applying orthopaedic subspecialty knowledge at a PG level.
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Affiliation(s)
- Raju Vaishya
- Department of Orthopaedics, Indraprastha Apollo Hospitals, Sarita Vihar, New Delhi, 110076, India.
| | - Karthikeyan P Iyengar
- Department of Orthopaedics, Southport and Ormskirk Hospital, Mersey West Lancashire Teaching NHS Trust, Southport, UK
| | | | - Rajesh Botchu
- Department of Musculoskeletal Radiology, Royal Orthopedic Hospital, Birmingham, UK
| | - Kapil Shirodkar
- Department of Musculoskeletal Radiology, Royal Orthopedic Hospital, Birmingham, UK
| | | | - Abhishek Vaish
- Department of Orthopaedics, Indraprastha Apollo Hospitals, Sarita Vihar, New Delhi, 110076, India
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Cao S, Wei Y, Yue Y, Wang D, Xiong A, Zeng H. A Scientometric Worldview of Artificial Intelligence in Musculoskeletal Diseases Since the 21st Century. J Multidiscip Healthc 2024; 17:3193-3211. [PMID: 39006873 PMCID: PMC11246091 DOI: 10.2147/jmdh.s477219] [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: 05/07/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
Purpose Over the past 24 years, significant advancements have been made in applying artificial intelligence (AI) to musculoskeletal (MSK) diseases. However, there is a lack of analytical and descriptive investigations on the trajectory, essential research directions, current research scenario, pivotal focuses, and future perspectives. This research aims to provide a thorough update on the progress in AI for MSK diseases over the last 24 years. Methods Data from the Web of Science database, covering January 1, 2000, to March 1, 2024, was analyzed. Using advanced analytical tools, we conducted comprehensive scientometric and visual analyses. Results The findings highlight the predominant influence of the USA, which accounts for 28.53% of the total publications and plays a key role in shaping research in this field. Notable productivity was seen at institutions such as the University of California, San Francisco, Harvard Medical School, and Seoul National University. Valentina Pedoia is identified as the most prolific contributor. Scientific Reports had the highest number of publications in this area. The five most significant diseases are joint diseases, bone fractures, bone tumors, cartilage diseases, and spondylitis. Conclusion This comprehensive scientometric assessment benefits both experienced researchers and newcomers, providing quick access to essential information and fostering the development of innovative concepts in this field.
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Affiliation(s)
- Siyang Cao
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Yihao Wei
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Yaohang Yue
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Deli Wang
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Ao Xiong
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Hui Zeng
- National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
- Department of Bone & Joint Surgery, Peking University Shenzhen Hospital, Shenzhen, Guangdong, People's Republic of China
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Zhao Q, Lan Y, Yin X, Wang K. Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis. BMC Med Imaging 2023; 23:208. [PMID: 38082213 PMCID: PMC10712108 DOI: 10.1186/s12880-023-01172-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 12/04/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND The gold standard to diagnose fatty liver is pathology. Recently, image-based artificial intelligence (AI) has been found to have high diagnostic performance. We systematically reviewed studies of image-based AI in the diagnosis of fatty liver. METHODS We searched the Cochrane Library, Pubmed, Embase and assessed the quality of included studies by QUADAS-AI. The pooled sensitivity, specificity, negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR) were calculated using a random effects model. Summary receiver operating characteristic curves (SROC) were generated to identify the diagnostic accuracy of AI models. RESULTS 15 studies were selected in our meta-analysis. Pooled sensitivity and specificity were 92% (95% CI: 90-93%) and 94% (95% CI: 93-96%), PLR and NLR were 12.67 (95% CI: 7.65-20.98) and 0.09 (95% CI: 0.06-0.13), DOR was 182.36 (95% CI: 94.85-350.61). After subgroup analysis by AI algorithm (conventional machine learning/deep learning), region, reference (US, MRI or pathology), imaging techniques (MRI or US) and transfer learning, the model also demonstrated acceptable diagnostic efficacy. CONCLUSION AI has satisfactory performance in the diagnosis of fatty liver by medical imaging. The integration of AI into imaging devices may produce effective diagnostic tools, but more high-quality studies are needed for further evaluation.
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Affiliation(s)
- Qi Zhao
- Department of Gastroenterology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China
- Department of Hepatology, Institute of Hepatology, Qilu Hospital of Shandong University, Shandong University, Wenhuaxi Road 107#, Jinan, Shandong, 250012, China
- Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, No. 324 Jingwu Road, Jinan, Shandong, 250021, China
- Shandong Booke Biotechnology Co. LTD, Liaocheng, Shandong, China
| | - Yadi Lan
- Department of Gastroenterology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, 250021, China
| | - Xunjun Yin
- Shandong Booke Biotechnology Co. LTD, Liaocheng, Shandong, China
| | - Kai Wang
- Department of Hepatology, Institute of Hepatology, Qilu Hospital of Shandong University, Shandong University, Wenhuaxi Road 107#, Jinan, Shandong, 250012, China.
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Hernigou P, Lustig S, Caton J. Artificial intelligence and robots like us (surgeons) for people like you (patients): toward a new human-robot-surgery shared experience. What is the moral and legal status of robots and surgeons in the operating room? INTERNATIONAL ORTHOPAEDICS 2023; 47:289-294. [PMID: 36637460 DOI: 10.1007/s00264-023-05690-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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