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Ghazizadeh E, Naseri Z, Deigner HP, Rahimi H, Altintas Z. Approaches of wearable and implantable biosensor towards of developing in precision medicine. Front Med (Lausanne) 2024; 11:1390634. [PMID: 39091290 PMCID: PMC11293309 DOI: 10.3389/fmed.2024.1390634] [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: 02/23/2024] [Accepted: 04/30/2024] [Indexed: 08/04/2024] Open
Abstract
In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes. This log embarks on a journey into the realm of wearable and implantable biosensors, where the convergence of biology and technology heralds a new dawn in personalized healthcare. Here, we explore the intricate web of innovations, challenges, and the immense potential these bioelectronics sentinels hold in sculpting the future of precision medicine.
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Affiliation(s)
- Elham Ghazizadeh
- Department of Bioinspired Materials and Biosensor Technologies, Faculty of Engineering, Institute of Materials Science, Kiel University, Kiel, Germany
- Department of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Naseri
- Department of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hans-Peter Deigner
- Institute of Precision Medicine, Furtwangen University, Villingen-Schwenningen, Germany
- Fraunhofer Institute IZI (Leipzig), Rostock, Germany
- Faculty of Science, Eberhard-Karls-University Tuebingen, Tuebingen, Germany
| | - Hossein Rahimi
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Zeynep Altintas
- Department of Bioinspired Materials and Biosensor Technologies, Faculty of Engineering, Institute of Materials Science, Kiel University, Kiel, Germany
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Ng IKS, Mok SF, Teo D. Competency in medical training: current concepts, assessment modalities, and practical challenges. Postgrad Med J 2024:qgae023. [PMID: 38376156 DOI: 10.1093/postmj/qgae023] [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: 12/19/2023] [Accepted: 01/23/2024] [Indexed: 02/21/2024]
Abstract
In the past two decades, competency-based medical education (CBME) has rapidly become the cornerstone of medical training and accreditation programmes worldwide. It has increasingly replaced traditional time-based educational approaches which were often rigid, fragmented, and overly emphasized clinical content knowledge over practical skillsets and attitudes. CBME adoption was in the hope of better preparing medical graduates for the demands and responsibilities of real-world clinical practice. For all the supposed merits of CBME, there hitherto remains difficulties in arriving at comprehensive and practical 'competency' definitions, and actual challenges with implementation of clinical competency assessment modalities pertaining to construct validity, reliability, and applicability with the use and interpretation of evaluation metrics. Therefore, in this article, we describe the various conceptualizations of 'competency' in medical education literature and attempt to refine its usage in practice to meet the evolving needs and expectations of healthcare stakeholders, as well as incorporate emerging concepts in the medical education discourse. We herein propose that clinical 'competencies' should be defined as multi-domain clinical expertise, comprising medical knowledge, skills, attitudes and metacognitive capabilities that reflects the prevailing needs of healthcare stakeholders, and is inferred from performance evaluations of medical trainees. In order to attain 'competence', there must then be a process of integrating multi-domain competencies into meaningful professional identity formation that is commensurate with the context and stage of medical training. In addition, we review the current competency assessment modalities, including common pitfalls with their usage, and sought to provide practical strategies to mitigate the identified challenges.
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Affiliation(s)
- Isaac K S Ng
- Department of Medicine, National University Hospital, Singapore 119228, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore 117597, Singapore
| | - Shao Feng Mok
- Yong Loo Lin School of Medicine, National University of Singapore 117597, Singapore
- Division of Endocrinology, Department of Medicine, National University Hospital, Singapore 119228, Singapore
| | - Desmond Teo
- Yong Loo Lin School of Medicine, National University of Singapore 117597, Singapore
- Fast and Chronic Programmes, Alexandra Hospital, Singapore 159964, Singapore
- Division of Advanced Internal Medicine, Department of Medicine, National University Hospital, Singapore 119228, Singapore
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Abdaljaleel M, Barakat M, Alsanafi M, Salim NA, Abazid H, Malaeb D, Mohammed AH, Hassan BAR, Wayyes AM, Farhan SS, Khatib SE, Rahal M, Sahban A, Abdelaziz DH, Mansour NO, AlZayer R, Khalil R, Fekih-Romdhane F, Hallit R, Hallit S, Sallam M. A multinational study on the factors influencing university students' attitudes and usage of ChatGPT. Sci Rep 2024; 14:1983. [PMID: 38263214 PMCID: PMC10806219 DOI: 10.1038/s41598-024-52549-8] [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: 09/30/2023] [Accepted: 01/19/2024] [Indexed: 01/25/2024] Open
Abstract
Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students' attitudes and usage of ChatGPT in Arab countries. The survey instrument "TAME-ChatGPT" was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the "TAME-ChatGPT" constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated "TAME-ChatGPT" as a useful tool for assessing ChatGPT adoption among university students. The successful integration of ChatGPT in higher education relies on the perceived ease of use, perceived usefulness, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks. Policies for ChatGPT adoption in higher education should be tailored to individual contexts, considering the variations in student attitudes observed in this study.
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Affiliation(s)
- Maram Abdaljaleel
- Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, 11942, Jordan
- Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Amman, 11942, Jordan
| | - Muna Barakat
- Department of Clinical Pharmacy and Therapeutics, Faculty of Pharmacy, Applied Science Private University, Amman, 11931, Jordan
| | - Mariam Alsanafi
- Department of Pharmacy Practice, Faculty of Pharmacy, Kuwait University, Kuwait City, Kuwait
- Department of Pharmaceutical Sciences, Public Authority for Applied Education and Training, College of Health Sciences, Safat, Kuwait
| | - Nesreen A Salim
- Prosthodontic Department, School of Dentistry, The University of Jordan, Amman, 11942, Jordan
- Prosthodontic Department, Jordan University Hospital, Amman, 11942, Jordan
| | - Husam Abazid
- Department of Clinical Pharmacy and Therapeutics, Faculty of Pharmacy, Applied Science Private University, Amman, 11931, Jordan
| | - Diana Malaeb
- College of Pharmacy, Gulf Medical University, P.O. Box 4184, Ajman, United Arab Emirates
| | - Ali Haider Mohammed
- School of Pharmacy, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia
| | | | | | - Sinan Subhi Farhan
- Department of Anesthesia, Al Rafidain University College, Baghdad, 10001, Iraq
| | - Sami El Khatib
- Department of Biomedical Sciences, School of Arts and Sciences, Lebanese International University, Bekaa, Lebanon
- Center for Applied Mathematics and Bioinformatics (CAMB), Gulf University for Science and Technology (GUST), 32093, Hawally, Kuwait
| | - Mohamad Rahal
- School of Pharmacy, Lebanese International University, Beirut, 961, Lebanon
| | - Ali Sahban
- School of Dentistry, The University of Jordan, Amman, 11942, Jordan
| | - Doaa H Abdelaziz
- Pharmacy Practice and Clinical Pharmacy Department, Faculty of Pharmacy, Future University in Egypt, Cairo, 11835, Egypt
- Department of Clinical Pharmacy, Faculty of Pharmacy, Al-Baha University, Al-Baha, Saudi Arabia
| | - Noha O Mansour
- Clinical Pharmacy and Pharmacy Practice Department, Faculty of Pharmacy, Mansoura University, Mansoura, 35516, Egypt
- Clinical Pharmacy and Pharmacy Practice Department, Faculty of Pharmacy, Mansoura National University, Dakahlia Governorate, 7723730, Egypt
| | - Reem AlZayer
- Clinical Pharmacy Practice, Department of Pharmacy, Mohammed Al-Mana College for Medical Sciences, 34222, Dammam, Saudi Arabia
| | - Roaa Khalil
- Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, 11942, Jordan
| | - Feten Fekih-Romdhane
- The Tunisian Center of Early Intervention in Psychosis, Department of Psychiatry "Ibn Omrane", Razi Hospital, 2010, Manouba, Tunisia
- Faculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia
| | - Rabih Hallit
- School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon
- Department of Infectious Disease, Bellevue Medical Center, Mansourieh, Lebanon
- Department of Infectious Disease, Notre Dame des Secours, University Hospital Center, Byblos, Lebanon
| | - Souheil Hallit
- School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon
- Research Department, Psychiatric Hospital of the Cross, Jal Eddib, Lebanon
| | - Malik Sallam
- Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, 11942, Jordan.
- Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Amman, 11942, Jordan.
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Muharremi G, Meçani R, Muka T. The Buzz Surrounding Precision Medicine: The Imperative of Incorporating It into Evidence-Based Medical Practice. J Pers Med 2023; 14:53. [PMID: 38248754 PMCID: PMC10820165 DOI: 10.3390/jpm14010053] [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: 10/24/2023] [Revised: 12/17/2023] [Accepted: 12/28/2023] [Indexed: 01/23/2024] Open
Abstract
Precision medicine (PM), through the integration of omics and environmental data, aims to provide a more precise prevention, diagnosis, and treatment of disease. Currently, PM is one of the emerging approaches in modern healthcare and public health, with wide implications for health care delivery, public health policy making formulation, and entrepreneurial endeavors. In spite of its growing popularity and the buzz surrounding it, PM is still in its nascent phase, facing considerable challenges that need to be addressed and resolved for it to attain the acclaim for which it strives. In this article, we discuss some of the current methodological pitfalls of PM, including the use of big data, and provide a perspective on how these challenges can be overcome by bringing PM closer to evidence-based medicine (EBM). Furthermore, to maximize the potential of PM, we present real-world illustrations of how EBM principles can be integrated into a PM approach.
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Affiliation(s)
| | - Renald Meçani
- Epistudia, 3008 Bern, Switzerland; (G.M.); (R.M.)
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, 8010 Graz, Austria
| | - Taulant Muka
- Epistudia, 3008 Bern, Switzerland; (G.M.); (R.M.)
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Sallam M, Barakat M, Sallam M. Pilot Testing of a Tool to Standardize the Assessment of the Quality of Health Information Generated by Artificial Intelligence-Based Models. Cureus 2023; 15:e49373. [PMID: 38024074 PMCID: PMC10674084 DOI: 10.7759/cureus.49373] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2023] [Indexed: 12/01/2023] Open
Abstract
Background Artificial intelligence (AI)-based conversational models, such as Chat Generative Pre-trained Transformer (ChatGPT), Microsoft Bing, and Google Bard, have emerged as valuable sources of health information for lay individuals. However, the accuracy of the information provided by these AI models remains a significant concern. This pilot study aimed to test a new tool with key themes for inclusion as follows: Completeness of content, Lack of false information in the content, Evidence supporting the content, Appropriateness of the content, and Relevance, referred to as "CLEAR", designed to assess the quality of health information delivered by AI-based models. Methods Tool development involved a literature review on health information quality, followed by the initial establishment of the CLEAR tool, which comprised five items that aimed to assess the following: completeness, lack of false information, evidence support, appropriateness, and relevance. Each item was scored on a five-point Likert scale from excellent to poor. Content validity was checked by expert review. Pilot testing involved 32 healthcare professionals using the CLEAR tool to assess content on eight different health topics deliberately designed with varying qualities. The internal consistency was checked with Cronbach's alpha (α). Feedback from the pilot test resulted in language modifications to improve the clarity of the items. The final CLEAR tool was used to assess the quality of health information generated by four distinct AI models on five health topics. The AI models were ChatGPT 3.5, ChatGPT 4, Microsoft Bing, and Google Bard, and the content generated was scored by two independent raters with Cohen's kappa (κ) for inter-rater agreement. Results The final five CLEAR items were: (1) Is the content sufficient?; (2) Is the content accurate?; (3) Is the content evidence-based?; (4) Is the content clear, concise, and easy to understand?; and (5) Is the content free from irrelevant information? Pilot testing on the eight health topics revealed acceptable internal consistency with a Cronbach's α range of 0.669-0.981. The use of the final CLEAR tool yielded the following average scores: Microsoft Bing (mean=24.4±0.42), ChatGPT-4 (mean=23.6±0.96), Google Bard (mean=21.2±1.79), and ChatGPT-3.5 (mean=20.6±5.20). The inter-rater agreement revealed the following Cohen κ values: for ChatGPT-3.5 (κ=0.875, P<.001), ChatGPT-4 (κ=0.780, P<.001), Microsoft Bing (κ=0.348, P=.037), and Google Bard (κ=.749, P<.001). Conclusions The CLEAR tool is a brief yet helpful tool that can aid in standardizing testing of the quality of health information generated by AI-based models. Future studies are recommended to validate the utility of the CLEAR tool in the quality assessment of AI-generated health-related content using a larger sample across various complex health topics.
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Affiliation(s)
- Malik Sallam
- Department of Pathology, Microbiology, and Forensic Medicine, School of Medicine, University of Jordan, Amman, JOR
- Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Amman, JOR
| | - Muna Barakat
- Department of Clinical Pharmacy and Therapeutics, School of Pharmacy, Applied Science Private University, Amman, JOR
- Department of Research, Middle East University, Amman, JOR
| | - Mohammed Sallam
- Department of Pharmacy, Mediclinic Parkview Hospital, Mediclinic Middle East, Dubai, ARE
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Meijer C, Uh HW, el Bouhaddani S. Digital Twins in Healthcare: Methodological Challenges and Opportunities. J Pers Med 2023; 13:1522. [PMID: 37888133 PMCID: PMC10608065 DOI: 10.3390/jpm13101522] [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: 08/16/2023] [Revised: 10/14/2023] [Accepted: 10/15/2023] [Indexed: 10/28/2023] Open
Abstract
One of the most promising advancements in healthcare is the application of digital twin technology, offering valuable applications in monitoring, diagnosis, and development of treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. In this review article, we consider digital twins as virtual counterparts of real human patients. The primary aim of this narrative review is to give an in-depth look into the various data sources and methodologies that contribute to the construction of digital twins across several healthcare domains. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation.
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Affiliation(s)
| | | | - Said el Bouhaddani
- Department Data Science & Biostatistics, Julius Center, UMC Utrecht, 3584 CX Utrecht, The Netherlands (H.-W.U.)
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