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Zhang H, Liu Y, Gu R. Correlation between psychological traits and the use of smart medical services in young and middle-aged adults: An observational study. World J Psychiatry 2024; 14:1224-1232. [PMID: 39165550 PMCID: PMC11331391 DOI: 10.5498/wjp.v14.i8.1224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 05/28/2024] [Accepted: 07/10/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Psychological problems affect economic development. However, there is a huge gap between mental health service resources and mental health service needs. Existing mental health service technology and platforms cannot meet all the diverse mental health needs of people. Smart medicine is a new medical system based online that can effectively improve the quality and efficiency of medical services and make mental health services accessible. AIM To explore the level of intelligent medical use among young and middle-aged people and its correlation with psychological factors. METHODS Convenience sampling was used to select 200 young and middle-aged patients with medical experience at the Third People's Hospital of Chengdu between January 2022 and January 2023 as the research subjects. The general condition Questionnaire, Eysenck Personality Questionnaire, Symptom Checklist-90, General Health Questionnaire, and Smart Medical Service Use Intention Questionnaire were used to collect data. Pearson's correlation was used to analyze the correlation between the participants' willingness to use smart medical services and their personality characteristics, psychological symptoms, and mental health. RESULTS The results revealed that the mental health of young and middle-aged people was poor, and some had psychological problems such as anxiety, depression, and physical discomfort. Familiarity, acceptance, and usage of smart healthcare in this population are at a medium level, and these levels correlate with psychological characteristics. Acceptance was positively correlated with E, and negatively correlated with P, anxiety, fear, anxiety/insomnia, and social dysfunction. The degree of use was negatively correlated with P, obsessive-compulsive symptoms, depression, anxiety, hostility, paranoia, and somatic symptoms. CONCLUSION The familiarity, acceptance, and usage of smart medical services among the middle-aged and young groups are related to various psychological characteristics.
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Affiliation(s)
- Hu Zhang
- Department of Internet Hospital Management, The Third People's Hospital of Chengdu, Chengdu 610031, Sichuan Province, China
| | - Yan Liu
- Department of Neurology, The Third People's Hospital of Chengdu, The Affiliated Hospital of Southwest Jiaotong University, Chengdu 610031, Sichuan Province, China
| | - Rui Gu
- Department of Neurology, The Third People's Hospital of Chengdu, The Affiliated Hospital of Southwest Jiaotong University, Chengdu 610031, Sichuan Province, China
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Almalawi A, Zafar A, Unhelkar B, Hassan S, Alqurashi F, Khan AI, Fahad A, Alam MM. Enhancing security in smart healthcare systems: Using intelligent edge computing with a novel Salp Swarm Optimization and radial basis neural network algorithm. Heliyon 2024; 10:e33792. [PMID: 39040324 PMCID: PMC11261867 DOI: 10.1016/j.heliyon.2024.e33792] [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: 06/21/2024] [Accepted: 06/26/2024] [Indexed: 07/24/2024] Open
Abstract
A smart healthcare system (SHS) is a health service system that employs advanced technologies such as wearable devices, the Internet of Things (IoT), and mobile internet to dynamically access information and connect people and institutions related to healthcare, thereby actively managing and responding to medical ecosystem needs. Edge computing (EC) plays a significant role in SHS as it enables real-time data processing and analysis at the data source, which reduces latency and improves medical intervention speed. However, the integration of patient information, including electronic health records (EHRs), into the SHS framework induces security and privacy concerns. To address these issues, an intelligent EC framework was proposed in this study. The objective of this study is to accurately identify security threats and ensure secure data transmission in the SHS environment. The proposed EC framework leverages the effectiveness of Salp Swarm Optimization and Radial Basis Functional Neural Network (SS-RBFN) for enhancing security and data privacy. The proposed methodology commences with the collection of healthcare information, which is then pre-processed to ensure the consistency and quality of the database for further analysis. Subsequently, the SS-RBFN algorithm was trained using the pre-processed database to distinguish between normal and malicious data streams accurately, offering continuous monitoring in the SHS environment. Additionally, a Rivest-Shamir-Adelman (RSA) approach was applied to safeguard data against security threats during transmission to cloud storage. The proposed model was trained and validated using the IoT-based healthcare database available at Kaggle, and the experimental results demonstrated that it achieved 99.87 % accuracy, 99.76 % precision, 99.49 % f-measure, 98.99 % recall, 97.37 % throughput, and 1.2s latency. Furthermore, the results achieved by the proposed model were compared with the existing models to validate its effectiveness in enhancing security.
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Affiliation(s)
- Abdulmohsen Almalawi
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - Aasim Zafar
- Department of Computer Science, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Bhuvan Unhelkar
- Muma School of Business, University of South Florida, Sarasota-Manatee Campus, Sarasota, FL, 33620, USA
| | - Shabbir Hassan
- Department of Computer Science, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Fahad Alqurashi
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
| | - Asif Irshad Khan
- Department of Computer Science, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Adil Fahad
- Department of Computer Science, College of Computer Science & Information Technology, Al Baha University, Al Baha, 65527, Saudi Arabia
| | - Md Mottahir Alam
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
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Haider SA, Pressman SM, Borna S, Gomez-Cabello CA, Sehgal A, Leibovich BC, Forte AJ. Evaluating Large Language Model (LLM) Performance on Established Breast Classification Systems. Diagnostics (Basel) 2024; 14:1491. [PMID: 39061628 PMCID: PMC11275570 DOI: 10.3390/diagnostics14141491] [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: 04/12/2024] [Revised: 06/25/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
Medical researchers are increasingly utilizing advanced LLMs like ChatGPT-4 and Gemini to enhance diagnostic processes in the medical field. This research focuses on their ability to comprehend and apply complex medical classification systems for breast conditions, which can significantly aid plastic surgeons in making informed decisions for diagnosis and treatment, ultimately leading to improved patient outcomes. Fifty clinical scenarios were created to evaluate the classification accuracy of each LLM across five established breast-related classification systems. Scores from 0 to 2 were assigned to LLM responses to denote incorrect, partially correct, or completely correct classifications. Descriptive statistics were employed to compare the performances of ChatGPT-4 and Gemini. Gemini exhibited superior overall performance, achieving 98% accuracy compared to ChatGPT-4's 71%. While both models performed well in the Baker classification for capsular contracture and UTSW classification for gynecomastia, Gemini consistently outperformed ChatGPT-4 in other systems, such as the Fischer Grade Classification for gender-affirming mastectomy, Kajava Classification for ectopic breast tissue, and Regnault Classification for breast ptosis. With further development, integrating LLMs into plastic surgery practice will likely enhance diagnostic support and decision making.
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Affiliation(s)
- Syed Ali Haider
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - Sahar Borna
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - Ajai Sehgal
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
| | - Bradley C. Leibovich
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
- Department of Urology, Mayo Clinic, Rochester, MN 55905, USA
| | - Antonio Jorge Forte
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
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Luțenco V, Țocu G, Guliciuc M, Moraru M, Candussi IL, Dănilă M, Luțenco V, Dimofte F, Mihailov OM, Mihailov R. New Horizons of Artificial Intelligence in Medicine and Surgery. J Clin Med 2024; 13:2532. [PMID: 38731061 PMCID: PMC11084145 DOI: 10.3390/jcm13092532] [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: 03/06/2024] [Revised: 04/06/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024] Open
Abstract
Background: Ideas about Artificial intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided are becoming a bigger pool and we need artificial intelligence that will help us with its superhuman powers. Its interaction with medicine is improving more and more, with medicine being a domain that continues to be perfected. Materials and Methods: The most important databases were used to perform this detailed search that addresses artificial intelligence in the medical and surgical fields. Discussion: Machine learning, deep learning, neural networks and computer vision are some of the mechanisms that are becoming a trend in healthcare worldwide. Developed countries such as Japan, France and Germany have already implemented artificial intelligence in their medical systems. The help it gives is in medical diagnosis, patient monitoring, personalized therapy and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques and to choose the best surgical techniques. Conclusions: The goal is to predict complications, reduce diagnostic times, diagnose complex pathologies, guide surgeons intraoperatively and reduce medical errors. We are at the beginning of this, and the potential is enormous, but we must not forget the impediments that may appear and slow down its implementation.
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Affiliation(s)
- Valerii Luțenco
- Surgery I Clinic, Emergency Hospital “Sf. Ap. Andrei”, 800578 Galați, Romania; (V.L.); (R.M.)
| | - George Țocu
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Mădălin Guliciuc
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Monica Moraru
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Iuliana Laura Candussi
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
- Clinical Children Emergency Hospital “Sf. Ioan”, 060011 Galați, Romania;
| | - Marius Dănilă
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
- Clinical Children Emergency Hospital “Sf. Ioan”, 060011 Galați, Romania;
| | - Verginia Luțenco
- Clinical Children Emergency Hospital “Sf. Ioan”, 060011 Galați, Romania;
| | - Florentin Dimofte
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Oana Mariana Mihailov
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Raul Mihailov
- Surgery I Clinic, Emergency Hospital “Sf. Ap. Andrei”, 800578 Galați, Romania; (V.L.); (R.M.)
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
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Galbusera F, Cina A. Image annotation and curation in radiology: an overview for machine learning practitioners. Eur Radiol Exp 2024; 8:11. [PMID: 38316659 PMCID: PMC10844188 DOI: 10.1186/s41747-023-00408-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/07/2023] [Indexed: 02/07/2024] Open
Abstract
"Garbage in, garbage out" summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and de-identified, considering local regulations. This narrative review presents a summary of the techniques that are used to ensure that all these requirements are fulfilled, with special emphasis on radiological imaging and freely available software solutions that can be directly employed by the interested researcher. Topics discussed include key imaging concepts, such as image resolution and pixel depth; file formats for medical image data storage; free software solutions for medical image processing; anonymisation and pseudonymisation to protect patient privacy, including compliance with regulations such as the Regulation (EU) 2016/679 "General Data Protection Regulation" (GDPR) and the 1996 United States Act of Congress "Health Insurance Portability and Accountability Act" (HIPAA); methods to eliminate patient-identifying features within images, like facial structures; free and commercial tools for image annotation; and techniques for data harmonisation and normalisation.Relevance statement This review provides an overview of the methods and tools that can be used to ensure high-quality data for machine learning and artificial intelligence applications in radiology.Key points• High-quality datasets are essential for reliable artificial intelligence algorithms in medical imaging.• Software tools like ImageJ and 3D Slicer aid in processing medical images for AI research.• Anonymisation techniques protect patient privacy during dataset preparation.• Machine learning models can accelerate image annotation, enhancing efficiency and accuracy.• Data curation ensures dataset integrity, compliance, and quality for artificial intelligence development.
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Affiliation(s)
- Fabio Galbusera
- Spine Center, Schulthess Clinic, Lengghalde 2, Zurich, 8008, Switzerland.
| | - Andrea Cina
- Spine Center, Schulthess Clinic, Lengghalde 2, Zurich, 8008, Switzerland
- ETH Zürich, Department of Health Sciences and Technologies, Zurich, Switzerland
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Iserson KV. Informed consent for artificial intelligence in emergency medicine: A practical guide. Am J Emerg Med 2024; 76:225-230. [PMID: 38128163 DOI: 10.1016/j.ajem.2023.11.022] [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: 10/12/2023] [Revised: 11/08/2023] [Accepted: 11/16/2023] [Indexed: 12/23/2023] Open
Abstract
As artificial intelligence (AI) expands its presence in healthcare, particularly within emergency medicine (EM), there is growing urgency to explore the ethical and practical considerations surrounding its adoption. AI holds the potential to revolutionize how emergency physicians (EPs) make clinical decisions, but AI's complexity often surpasses EPs' capacity to provide patients with informed consent regarding its use. This article underscores the crucial need to address the ethical pitfalls of AI in EM. Patient autonomy necessitates that EPs engage in conversations with patients about whether to use AI in their evaluation and treatment. As clinical AI integration expands, this discussion should become an integral part of the informed consent process, aligning with ethical and legal requirements. The rapid availability of AI programs, fueled by vast electronic health record (EHR) datasets, has led to increased pressure on hospitals and clinicians to embrace clinical AI without comprehensive system evaluation. However, the evolving landscape of AI technology outpaces our ability to anticipate its impact on medical practice and patient care. The central question arises: Are EPs equipped with the necessary knowledge to offer well-informed consent regarding clinical AI? Collaborative efforts between EPs, bioethicists, AI researchers, and healthcare administrators are essential for the development and implementation of optimal AI practices in EM. To facilitate informed consent about AI, EPs should understand at least seven key areas: (1) how AI systems operate; (2) whether AI systems are understandable and trustworthy; (3) the limitations of and errors AI systems make; (4) how disagreements between the EP and AI are resolved; (5) whether the patient's personally identifiable information (PII) and the AI computer systems will be secure; (6) if the AI system functions reliably (has been validated); and (7) if the AI program exhibits bias. This article addresses each of these critical issues, aiming to empower EPs with the knowledge required to navigate the intersection of AI and informed consent in EM.
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Affiliation(s)
- Kenneth V Iserson
- Professor Emeritus, Department of Emergency Medicine, The University of Arizona, Tucson, AZ, 4930 N. Calle Faja, Tucson, AZ, United States of America.
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He L, Eastburn M, Smirk J, Zhao H. Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0. SENSORS (BASEL, SWITZERLAND) 2023; 23:5754. [PMID: 37420917 DOI: 10.3390/s23125754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/05/2023] [Accepted: 06/15/2023] [Indexed: 07/09/2023]
Abstract
Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, medical devices, hospitals, clinics, medical suppliers, and other healthcare-related components. Body chemical sensor and biosensor networks (BSNs) provide the necessary platform for Healthcare 4.0 to collect various medical data from patients. BSN is the foundation of Healthcare 4.0 in raw data detection and information collecting. This paper proposes a BSN architecture with chemical sensors and biosensors to detect and communicate physiological measurements of human bodies. These measurement data help healthcare professionals to monitor patient vital signs and other medical conditions. The collected data facilitates disease diagnosis and injury detection at an early stage. Our work further formulates the problem of sensor deployment in BSNs as a mathematical model. This model includes parameter and constraint sets to describe patient body characteristics, BSN sensor features, as well as biomedical readout requirements. The proposed model's performance is evaluated by multiple sets of simulations on different parts of the human body. Simulations are designed to represent typical BSN applications in Healthcare 4.0. Simulation results demonstrate the impact of various biofactors and measurement time on sensor selections and readout performance.
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Affiliation(s)
- Lawrence He
- Princeton High School, Princeton, NJ 08540, USA
| | | | - James Smirk
- Princeton High School, Princeton, NJ 08540, USA
| | - Hong Zhao
- Gildart Haase School of Computer Sciences and Engineering, Fairleigh Dickinson University, Teaneck, NJ 07666, USA
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Li S, Li R, Zhu B, Zhang B, Li J, Liu F, Wei Y. Research on user's highly sensitive privacy disclosure intention in home intelligent health service system: A perspective from trust enhancement mechanism. Digit Health 2023; 9:20552076231219444. [PMID: 38107984 PMCID: PMC10722956 DOI: 10.1177/20552076231219444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 11/21/2023] [Indexed: 12/19/2023] Open
Abstract
Objective The aim is to investigate the determinants and mechanisms that influence user's highly sensitive privacy disclosure intention (HSPDI) in home intelligent health service system (HIHSS). Methods This study improves the privacy calculus theory by considering the influence of service providers' trust enhancement mechanism besides benefit and risk factors and investigates their impact on users' HSPDIs. This study takes perceived valence and perceived security as the trade-off result among perceived benefits, perceived risks, financial trust enhancement mechanism, and the technical trust enhancement mechanism and suggests that perceived valence and perceived security further affect users' HSPDI in HIHSS. Moreover, the common and differential effects of the perceived justice of privacy violation compensation (PJOPVC) and the perceived effectiveness of privacy protection technologies (PEOPPTs) are studied. The structural equation model is used to analyze 204 valid samples to test the proposed model. Results The results show that perceived benefits and perceived risks are important predictors of perceived valence and perceived security, and further affect users' HSPDI. We find PJOPVC has a greater impact on perceived valence while PEOPPT has a greater impact on perceived security. Conclusions We recommend that the HSPDI of users with low perceived valence can be improved by providing privacy violation compensation while the HSPDI of users with low perceived security can be enhanced by popularizing relevant knowledge of privacy protection technologies.
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Affiliation(s)
- Shugang Li
- School of Management, Shanghai University, Shanghai, China
| | - Ruoxuan Li
- School of Management, Shanghai University, Shanghai, China
| | - Boyi Zhu
- School of Management, Shanghai University, Shanghai, China
| | - Beiyan Zhang
- School of Management, Shanghai University, Shanghai, China
| | - Jiayi Li
- Shanghai Songjiang No.2 High School, Shanghai, China
| | - Fang Liu
- School of Management, Shanghai University, Shanghai, China
| | - Yanfang Wei
- School of Management, Shanghai University, Shanghai, China
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Bitkina OV, Park J, Kim HK. Application of artificial intelligence in medical technologies: A systematic review of main trends. Digit Health 2023; 9:20552076231189331. [PMID: 37485326 PMCID: PMC10359663 DOI: 10.1177/20552076231189331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 06/30/2023] [Indexed: 07/25/2023] Open
Abstract
Objective Artificial intelligence (AI) has been increasingly applied in various fields of science and technology. In line with the current research, medicine involves an increasing number of artificial intelligence technologies. The introduction of rapid AI can lead to positive and negative effects. This is a multilateral analytical literature review aimed at identifying the main branches and trends in the use of using artificial intelligence in medical technologies. Methods The total number of literature sources reviewed is n = 89, and they are analyzed based on the literature reporting evidence-based guideline PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for a systematic review. Results As a result, from the initially selected 198 references, 155 references were obtained from the databases and the remaining 43 sources were found on open internet as direct links to publications. Finally, 89 literature sources were evaluated after exclusion of unsuitable references based on the duplicated and generalized information without focusing on the users. Conclusions This article is identifying the current state of artificial intelligence in medicine and prospects for future use. The findings of this review will be useful for healthcare and AI professionals for improving the circulation and use of medical AI from design to implementation stage.
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Affiliation(s)
- Olga Vl Bitkina
- Department of Industrial and Management Engineering, Incheon National University, Incheon, Korea
| | - Jaehyun Park
- Department of Industrial and Management Engineering, Incheon National University, Incheon, Korea
| | - Hyun K. Kim
- School of Information Convergence, Kwangwoon University, Seoul, Korea
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