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Demir-Kaymak Z, Turan Z, Unlu-Bidik N, Unkazan S. Effects of midwifery and nursing students' readiness about medical Artificial intelligence on Artificial intelligence anxiety. Nurse Educ Pract 2024; 78:103994. [PMID: 38810350 DOI: 10.1016/j.nepr.2024.103994] [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: 02/21/2024] [Revised: 04/30/2024] [Accepted: 05/07/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND Artificial intelligence technologies are one of the most important technologies of today. Developments in artificial intelligence technologies have widespread and increased the use of artificial intelligence in many areas. The field of health is also one of the areas where artificial intelligence technologies are widely used. For this reason, it is considered important that healthcare professionals be prepared for artificial intelligence and do not experience problems while training them. In this study, midwife and nurse candidates, as future healthcare professionals, were discussed. AIM This study aims to examine the effect of the artificial intelligence readiness on the artificial intelligence anxiety and the effect of artificial intelligence characteristic variables (artificial intelligence knowledge, daily life, occupational threat, artificial intelligence trust) on the medical artificial intelligence readiness and artificial intelligence anxiety of students. METHODS This study was planned and carried out as a relational survey study, which is a quantitative research. A total of 480 students, consisting of 240 nursing and 240 midwifery students, were included in this study. SPSS 26.0 and AMOS 26 package programs were used to analyse the data and descriptive statistics (frequency, percentage, mean, standard deviation) and path analysis for the structural equation model were used. RESULTS No significant difference was found between the medical artificial intelligence readiness (p=0.082) and artificial intelligence anxiety (p=0.486) scores of midwifery and nursing students. The model of the relationship between medical artificial intelligence readiness and artificial intelligence anxiety had a good goodness of fit. Artificial intelligence knowledge and using artificial intelligence in daily life are predictors of medical artificial intelligence readiness. Using artificial intelligence in daily life, occupational threat and artificial intelligence trust are predictors of artificial intelligence anxiety. CONCLUSION Midwifery and nursing students' AI anxiety and AI readiness levels were found to be at a moderate level and students' AI readiness affected AI anxiety.
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Affiliation(s)
- Zeliha Demir-Kaymak
- Sakarya University Faculty of Education, Department of Computer Education and Instructional Technologies, Sakarya, Turkiye.
| | - Zekiye Turan
- Sakarya University, Faculty of Health Sciences, Department of Nursing, Sakarya, Turkiye
| | - Nazli Unlu-Bidik
- Sakarya University, Faculty of Health Sciences, Department of Midwifery, Sakarya, Turkiye
| | - Semiha Unkazan
- Sakarya University, Faculty of Health Sciences, Department of Nursing, Sakarya, Turkiye
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Pop-Jordanova N. Opportunity to Use Artificial Intelligence in Medicine. Pril (Makedon Akad Nauk Umet Odd Med Nauki) 2024; 45:5-13. [PMID: 39008641 DOI: 10.2478/prilozi-2024-0009] [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] [Indexed: 07/17/2024]
Abstract
Over the past period different reports related to the artificial intelligence (AI) and machine learning used in everyday life have been growing intensely. However, the AI in our country is still very limited, especially in the field of medicine. The aim of this article is to give some review about AI in medicine and the related fields based on published articles in PubMed and Psych Net. A research showed more than 9 thousand articles available at the mentioned databases. After providing some historical data, different AI applications in different fields of medicine are discussed. Finally, some limitations and ethical implications are discussed.
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Paul S, Govindaraj S, Jk J. ChatGPT Versus National Eligibility cum Entrance Test for Postgraduate (NEET PG). Cureus 2024; 16:e63048. [PMID: 39050297 PMCID: PMC11268980 DOI: 10.7759/cureus.63048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2024] [Indexed: 07/27/2024] Open
Abstract
Introduction With both suspicion and excitement, artificial intelligence tools are being integrated into nearly every aspect of human existence, including medical sciences and medical education. The newest large language model (LLM) in the class of autoregressive language models is ChatGPT. While ChatGPT's potential to revolutionize clinical practice and medical education is under investigation, further research is necessary to understand its strengths and limitations in this field comprehensively. Methods Two hundred National Eligibility cum Entrance Test for Postgraduate 2023 questions were gathered from various public education websites and individually entered into Microsoft Bing (GPT-4 Version 2.2.1). Microsoft Bing Chatbot is currently the only platform incorporating all of GPT-4's multimodal features, including image recognition. The results were subsequently analyzed. Results Out of 200 questions, ChatGPT-4 answered 129 correctly. The most tested specialties were medicine (15%), obstetrics and gynecology (15%), general surgery (14%), and pathology (10%), respectively. Conclusion This study sheds light on how well the GPT-4 performs in addressing the NEET-PG entrance test. ChatGPT has potential as an adjunctive instrument within medical education and clinical settings. Its capacity to react intelligently and accurately in complicated clinical settings demonstrates its versatility.
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Affiliation(s)
- Sam Paul
- General Surgery, St John's Medical College Hospital, Bengaluru, IND
| | - Sridar Govindaraj
- Surgical Gastroenterology and Laparoscopy, St John's Medical College Hospital, Bengaluru, IND
| | - Jerisha Jk
- Pediatrics and Neonatology, Christian Medical College Ludhiana, Ludhiana, IND
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Kamel MA, Abbas MT, Kanaan CN, Awad KA, Baba Ali N, Scalia IG, Farina JM, Pereyra M, Mahmoud AK, Steidley DE, Rosenthal JL, Ayoub C, Arsanjani R. How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges. J Cardiovasc Dev Dis 2024; 11:118. [PMID: 38667736 PMCID: PMC11050851 DOI: 10.3390/jcdd11040118] [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: 02/29/2024] [Revised: 04/09/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.
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Affiliation(s)
- Moaz A. Kamel
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | | | | | - Kamal A. Awad
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Nima Baba Ali
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Isabel G. Scalia
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Juan M. Farina
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Milagros Pereyra
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Ahmed K. Mahmoud
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - D. Eric Steidley
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Julie L. Rosenthal
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Chadi Ayoub
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
- Division of Cardiovascular Imaging, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
| | - Reza Arsanjani
- Department of Cardiovascular Medicine, Mayo Clinic, Phoenix, AZ 85054, USA
- Division of Cardiovascular Imaging, Mayo Clinic, 5777 East Mayo Boulevard, Phoenix, AZ 85054, USA
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Baig Z, Lawrence D, Ganhewa M, Cirillo N. Accuracy of Treatment Recommendations by Pragmatic Evidence Search and Artificial Intelligence: An Exploratory Study. Diagnostics (Basel) 2024; 14:527. [PMID: 38472998 DOI: 10.3390/diagnostics14050527] [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: 01/02/2024] [Revised: 02/18/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
There is extensive literature emerging in the field of dentistry with the aim to optimize clinical practice. Evidence-based guidelines (EBGs) are designed to collate diagnostic criteria and clinical treatment for a range of conditions based on high-quality evidence. Recently, advancements in Artificial Intelligence (AI) have instigated further queries into its applicability and integration into dentistry. Hence, the aim of this study was to develop a model that can be used to assess the accuracy of treatment recommendations for dental conditions generated by individual clinicians and the outcomes of AI outputs. For this pilot study, a Delphi panel of six experts led by CoTreat AI provided the definition and developed evidence-based recommendations for subgingival and supragingival calculus. For the rapid review-a pragmatic approach that aims to rapidly assess the evidence base using a systematic methodology-the Ovid Medline database was searched for subgingival and supragingival calculus. Studies were selected and reported based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), and this study complied with the minimum requirements for completing a restricted systematic review. Treatment recommendations were also searched for these same conditions in ChatGPT (version 3.5 and 4) and Bard (now Gemini). Adherence to the recommendations of the standard was assessed using qualitative content analysis and agreement scores for interrater reliability. Treatment recommendations by AI programs generally aligned with the current literature, with an agreement of up to 75%, although data sources were not provided by these tools, except for Bard. The clinician's rapid review results suggested several procedures that may increase the likelihood of overtreatment, as did GPT4. In terms of overall accuracy, GPT4 outperformed all other tools, including rapid review (Cohen's kappa 0.42 vs. 0.28). In summary, this study provides preliminary observations for the suitability of different evidence-generating methods to inform clinical dental practice.
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Affiliation(s)
- Zunaira Baig
- Melbourne Dental School, The University of Melbourne, 720 Swanston Street, Carlton, VIC 3053, Australia
| | | | | | - Nicola Cirillo
- Melbourne Dental School, The University of Melbourne, 720 Swanston Street, Carlton, VIC 3053, Australia
- CoTreat Pty Ltd., Melbourne, VIC 3000, Australia
- School of Dentistry, University of Jordan, Amman 11733, Jordan
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Alam MK, Alftaikhah SAA, Issrani R, Ronsivalle V, Lo Giudice A, Cicciù M, Minervini G. Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: A systematic review and meta-analysis of in-vitro studies. Heliyon 2024; 10:e24221. [PMID: 38317889 PMCID: PMC10838702 DOI: 10.1016/j.heliyon.2024.e24221] [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: 09/30/2023] [Revised: 01/02/2024] [Accepted: 01/04/2024] [Indexed: 02/07/2024] Open
Abstract
Background In the past, dentistry heavily relied on manual image analysis and diagnostic procedures, which could be time-consuming and prone to human error. The advent of artificial intelligence (AI) has brought transformative potential to the field, promising enhanced accuracy and efficiency in various dental imaging tasks. This systematic review and meta-analysis aimed to comprehensively evaluate the applications of AI in dental imaging modalities, focusing on in-vitro studies. Methods A systematic literature search was conducted, in accordance with the PRISMA guidelines. The following databases were systematically searched: PubMed/MEDLINE, Embase, Web of Science, Scopus, IEEE Xplore, Cochrane Library, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Google Scholar. The meta-analysis employed fixed-effects models to assess AI accuracy, calculating odds ratios (OR) for true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) with 95 % confidence intervals (CI). Heterogeneity and overall effect tests were applied to ensure the reliability of the findings. Results 9 studies were selected that encompassed various objectives, such as tooth segmentation and classification, caries detection, maxillofacial bone segmentation, and 3D surface model creation. AI techniques included convolutional neural networks (CNNs), deep learning algorithms, and AI-driven tools. Imaging parameters assessed in these studies were specific to the respective dental tasks. The analysis of combined ORs indicated higher odds of accurate dental image assessments, highlighting the potential for AI to improve TPR, TNR, PPV, and NPV. The studies collectively revealed a statistically significant overall effect in favor of AI in dental imaging applications. Conclusion In summary, this systematic review and meta-analysis underscore the transformative impact of AI on dental imaging. AI has the potential to revolutionize the field by enhancing accuracy, efficiency, and time savings in various dental tasks. While further research in clinical settings is needed to validate these findings and address study limitations, the future implications of integrating AI into dental practice hold great promise for advancing patient care and the field of dentistry.
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Affiliation(s)
- Mohammad Khursheed Alam
- Preventive Dentistry Department, College of Dentistry, Jouf University, Sakaka, 72345, Saudi Arabia
- Department of Dental Research Cell, Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College and Hospitals, Chennai, 600077, India
- Department of Public Health, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, 1207, Bangladesh
| | | | - Rakhi Issrani
- Preventive Dentistry Department, College of Dentistry, Jouf University, Sakaka, 72345, Saudi Arabia
| | - Vincenzo Ronsivalle
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, Italy
| | - Antonino Lo Giudice
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, Italy
| | - Marco Cicciù
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, Italy
| | - Giuseppe Minervini
- Multidisciplinary Department of Medical-Surgical and Odontostomatological Specialties, University of Campania “Luigi Vanvitelli”, 80121, Naples, Italy
- Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Science (SIMATS), Saveetha University, Chennai, Tamil Nadu, India
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Sogi GM. Decipher the Cipher. Contemp Clin Dent 2024; 15:1-2. [PMID: 38707670 PMCID: PMC11068239 DOI: 10.4103/ccd.ccd_112_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024] Open
Affiliation(s)
- Girish Malleshappa Sogi
- Editor-in-chief, Contemporary Clinical Dentistry, Principal cum Dean, MM College of Dental Sciences and Research, Maharishi Markandeshwar (Deemed to be University), Ambala, Haryana, India E-mail:
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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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9
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Affiliation(s)
- Mojtaba Dorri
- Honorary Associate Professor/Consultant in Restorative Dentistry (Prosthodontics, Endodontics, Periodontology and Implantology), Bristol Dental Hospital, Lower Maudlin Street, Bristol, BS1 2LY, UK.
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10
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Vodanović M, Subašić M, Milošević DP, Galić I, Brkić H. Artificial intelligence in forensic medicine and forensic dentistry. THE JOURNAL OF FORENSIC ODONTO-STOMATOLOGY 2023; 41:30-41. [PMID: 37634174 PMCID: PMC10473456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
This review article aims to highlight the current possibilities for applying Artificial Intelligence in modern forensic medicine and forensic dentistry and present the advantages and disadvantages of its use. For this purpose, the relevant academic literature was searched using PubMed, Web of Science and Scopus. The application of Artificial Intelligence in forensic medicine and forensic dentistry is still in its early stages. However, the possibilities are great, and the future will show what is applicable in daily practice. Artificial Intelligence will improve the accuracy and efficiency of work in forensic medicine and forensic dentistry; it can automate some tasks; and enhance the quality of evidence. Disadvantages of the application of Artificial Intelligence may be related to discrimination, transparency, accountability, privacy, security, ethics and others. Artificial Intelligence systems should be used as a support tool, not as a replacement for forensic experts.
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Affiliation(s)
- M Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
| | - M Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - D P Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - I Galić
- School of Medicine, University of Split, Croatia
| | - H Brkić
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
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Saeed A, Alkhurays M, AlMutlaqah M, AlAzbah M, Alajlan SA. Future of Using Robotic and Artificial Intelligence in Implant Dentistry. Cureus 2023; 15:e43209. [PMID: 37700959 PMCID: PMC10494478 DOI: 10.7759/cureus.43209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/09/2023] [Indexed: 09/14/2023] Open
Abstract
The integration of robotic technology and artificial intelligence (AI) in implant dentistry has ushered in a new era of precision and efficiency. This abstract aims to understand the integration, implications, potential, and challenges of robotic technology and AI in implant dentistry. Robotic systems offer unparalleled accuracy in implant placement, reducing human error and improving treatment outcomes. AI algorithms analyze extensive patient data to assist in diagnosis, treatment planning, and implant design, optimizing the overall process. Successful case studies demonstrate improved implant survival rates and patient satisfaction. However, ethical considerations and the balance between human expertise and reliance on technology must be addressed. Ongoing research aims to enhance these technologies and integrate them with digital workflows. Collaboration and knowledge sharing among practitioners, researchers, and industry experts are essential to drive progress and ensure responsible implementation. The future of implant dentistry lies in harnessing the potential of robotics and AI while upholding the highest standards of patient care and ethical practice.
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