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The July 2022 issue of the Singapore Medical Journal (SMJ) marks a turning point in its history. As the official journal of the Singapore Medical Association (SMA), the SMJ was first published in March 1960 and started out as a quarterly publication. To better meet the needs of its authors and readers, it became a bimonthly publication in 1980 and has been published monthly since 1997. All these years, the production of the journal has been handled inhouse. However, recent operational and staffing challenges have negatively affected our work processes and the turnaround time for the articles submitted by our esteemed authors. In consultation with and with the support of the SMA Council, the SMJ made the decision to partner with a publishing house in order to enhance our operational efficiency. This issue of the SMJ marks the start of our collaboration with Wolters Kluwer Medknow. Wolters Kluwer Medknow is a leading global publishing services provider that provides publishing services to more than 400 medical society journals in over 40 specialties. With the support of a publishing house and access to its dedicated resources, we anticipate that our turnaround time from acceptance to publication will improve. With more resources available, the SMJ will also work on enhancing manuscript submission and publishing processes. This year also marks another first. Clarivate’s Journal Citation Reports™ 2022 have been released recently. We are proud to announce that the SMJ continues to make steady progress. In the ‘Medicine, General and Internal’ category, we have moved up in the journal impact factor (IF) rankings from the third to the second quartile. Our latest IF has risen to an all-time high of 3.331, from 1.858 last year [Figure 1].[1] We note that journal IFs have been heavily influenced by coronavirus disease 2019 (COVID-19)-related publications in 2021. In general, the IF rankings of journals that did not focus excessively on COVID-19 research have remained stable. As a general medical journal, it is a cause for celebration that we continue to make steadfast progress.[2] This is a testament to the quality of submissions by our authors and the efforts of our reviewers.Figure 1: Chart shows the SMJ’s impact factor from 2010 to 2021.As we move forward into this new era, the SMJ will continue to focus on what matters to our community: the timely publication of quality papers that meet our educational needs and shape our practice. Although most of our manuscripts traditionally come from Singapore, in the last year, we have published papers from more than 30 countries around the world, according to the 2022 Clarivate report. We thank you – our readers, authors, reviewers, Editorial Board members and, importantly, our editorial staff – for the continued support.
The year 2023 draws to a close. The sense of relief from post-COVID-19 recovery has been tempered by the scale of post-recovery efforts, the sudden outbreak of unexpected wars and the existential threat of climate change. Such is the capriciousness of life. The only certainty is that we must always plan and be prepared for unforeseen challenges while seeking improvement to better chart our course. Singapore Medical Journal (SMJ) has held its course over the years. We are guided by our mission to publish quality medical research of relevance to our community, and to serve as a platform for continued professional development. In 2023, SMJ published a special issue on Genetics and Genomics in January 2023. As much as subspecialisation is needed to advance science and to manage complexities in clinical care, the generalists must also keep abreast with clinically relevant subspecialist advances in medical care, to better inform their patients and advocate for their interests. Topics that were addressed in this issue included pitfalls in clinical genetics,[1] genetics in prenatal diagnosis[2] and paediatric care,[3] genetics and cancer predisposition,[4] genetics in ocular diseases,[5] as well as genetics in the context of data science application[6] and microbiome research.[7] Another special issue focusing on obesity was published in March 2023 in conjunction with World Obesity Day. In addition to providing an overview of obesity,[8] other articles examined weight bias and stigma in healthcare professionals,[9] the current obesity treatment landscape in Singapore,[10] and what the global obesity agenda means for Singapore.[11] We hope that you have found these articles useful and an enjoyable read. We will continue to organise such thematic issues in the coming years to increase the depth of coverage and focus on important clinical topics. The journal stopped accepting submissions for Pictorial Essays in September 2023, as we felt that it has run its course, although we still welcome state-of-the-art radiology reviews and Clinics in Diagnostic Imaging articles. In addition, we have restructured the format of our Original Articles to include a summary box so that the key messages of the article will stand out. To better reach out to our authors and readers, we have expanded our social media team, and we encourage authors of Original Articles and Reviews to provide us with a tweetable summary, hashtags, short write-ups, striking images, and their social media handles to facilitate the promotion of their articles on social media. We are heartened by the growing number of followers and increased engagement on social media. The Editorial Board is the backbone of SMJ. We deeply appreciate the dedication of all our board members, who serve tirelessly despite their busy schedules. As part of the cycle of growth and renewal, in addition to having new members join us to strengthen the SMJ editorial, there will inevitably be departures. We would like to express our heartfelt thanks to Prof Tan Puay Hoon, Prof Hsu Pon Poh and Dr William Kong Kok Fai, who stepped down in December 2022, as well as A/Prof Samuel Tay Sam Wah, who will step down at the end of this year. As we wind down our activities for the year, let us take a well-deserved break and recharge. We wish everyone happiness and peace, and we look forward to your continued support in 2024. We will not rest on our laurels and will continuously seek to improve SMJ and to better engage with everyone.
As medical students studying in the United Kingdom, we found the editorial "Singapore Medical Journal in the age of social media" Being medical students who use social media, we agree that the Internet brings opportunities and supports the Singapore Medical Journal in its efforts to engage with social media.
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Singapore Medical Journal
Singapore Medical Journal
Medical tourism is a growing phenomenon with policy implications for health systems, particularly of destination countries. Private actors and governments in Southeast Asia are promoting the medical tourist industry, but the potential impact on health systems, particularly in terms of equity in access and availability for local consumers, is unclear. This article presents a conceptual framework that outlines the policy implications of medical tourism's growth for health systems, drawing on the cases of Thailand, Singapore and Malaysia, three regional hubs for medical tourism, via an extensive review of academic and grey literature. Variables for further analysis of the potential impact of medical tourism on health systems are also identified. The framework can provide a basis for empirical, in country studies weighing the benefits and disadvantages of medical tourism for health systems. The policy implications described are of particular relevance for policymakers and industry practitioners in other Southeast Asian countries with similar health systems where governments have expressed interest in facilitating the growth of the medical tourist industry. This article calls for a universal definition of medical tourism and medical tourists to be enunciated, as well as concerted data collection efforts, to be undertaken prior to any meaningful empirical analysis of medical tourism's impact on health systems.
INTRODUCTION: Large language models, in particular ChatGPT, have showcased remarkable language processing capabilities. Given the substantial workload of university medical staff, this study aims to assess the quality of multiple-choice questions (MCQs) produced by ChatGPT for use in graduate medical examinations, compared to questions written by university professoriate staffs based on standard medical textbooks. METHODS: 50 MCQs were generated by ChatGPT with reference to two standard undergraduate medical textbooks (Harrison's, and Bailey & Love's). Another 50 MCQs were drafted by two university professoriate staff using the same medical textbooks. All 100 MCQ were individually numbered, randomized and sent to five independent international assessors for MCQ quality assessment using a standardized assessment score on five assessment domains, namely, appropriateness of the question, clarity and specificity, relevance, discriminative power of alternatives, and suitability for medical graduate examination. RESULTS: The total time required for ChatGPT to create the 50 questions was 20 minutes 25 seconds, while it took two human examiners a total of 211 minutes 33 seconds to draft the 50 questions. When a comparison of the mean score was made between the questions constructed by A.I. with those drafted by humans, only in the relevance domain that the A.I. was inferior to humans (A.I.: 7.56 +/- 0.94 vs human: 7.88 +/- 0.52; p = 0.04). There was no significant difference in question quality between questions drafted by A.I. versus humans, in the total assessment score as well as in other domains. Questions generated by A.I. yielded a wider range of scores, while those created by humans were consistent and within a narrower range. CONCLUSION: ChatGPT has the potential to generate comparable-quality MCQs for medical graduate examinations within a significantly shorter time.
The growth of global medical tourism in the recent years had spurred the interest of many governments to join in the bandwagon, particularly from Asia. Using the SWOT analytical model, this paper provides pertinent comparative analysis of the medical tourism destinations here being Malaysia, Thailand, Singapore and India. Each destination possesses its own value propositions to convince the demands of medical tourists. Malaysia and Thailand have a good mixture of elements (medical, tourism and wellness) to be an excellent medical tourism destination while Singapore and India need further development in some of these elements. Meeting or exceeding the medical tourists’ expectations and requirements are the priority of medical tourism destination marketers in ensuring a successful medical tourism industry development.
Th is study is aimed at providing a comparative insight into strategic advantages responsible for the competitiveness of medical tourism market of three selected Asian destinations -India, Singapore and Th ailand.Based on the examination of relevant literature and cross-country benchmarking analysis, a set of cross-functional and complex strategic resources and competencies were found responsible for the growing medical tourism competitiveness of these destinations.Th ese resources include qualities of medical specialties, obtained international accreditations, medical tourism sector infrastructure, and established reputation.Th e core competencies which have driven rivalry advantages range from the ability to off er holistic and wide-range of medical services to the ability of creating effi cient and interrelated health and tourism sectors.Conclusively, the study emphasizes that the mastery of building diff erent medical tourism strategic capabilities amongst these destinations inherently has been led through a clear-cut market orientation displayed and reinforced with sound and well-integrated strategies.Distinctively, Singapore's diff erentiation strategy has driven its advanced medical tourism system; Th ailand's best-cost provider strategy has molded its medical tourism attractiveness; while India's diversifi cation strategy and cost leadership has led to its long-standing market.
BACKGROUND: Since the advent of artificial intelligence (AI) in 1955, the applications of AI have increased over the years within a rapidly changing digital landscape where public expectations are on the rise, fed by social media, industry leaders, and medical practitioners. However, there has been little interest in AI in medical education until the last two decades, with only a recent increase in the number of publications and citations in the field. To our knowledge, thus far, a limited number of articles have discussed or reviewed the current use of AI in medical education. OBJECTIVE: This study aims to review the current applications of AI in medical education as well as the challenges of implementing AI in medical education. METHODS: Medline (Ovid), EBSCOhost Education Resources Information Center (ERIC) and Education Source, and Web of Science were searched with explicit inclusion and exclusion criteria. Full text of the selected articles was analyzed using the Extension of Technology Acceptance Model and the Diffusions of Innovations theory. Data were subsequently pooled together and analyzed quantitatively. RESULTS: A total of 37 articles were identified. Three primary uses of AI in medical education were identified: learning support (n=32), assessment of students' learning (n=4), and curriculum review (n=1). The main reasons for use of AI are its ability to provide feedback and a guided learning pathway and to decrease costs. Subgroup analysis revealed that medical undergraduates are the primary target audience for AI use. In addition, 34 articles described the challenges of AI implementation in medical education; two main reasons were identified: difficulty in assessing the effectiveness of AI in medical education and technical challenges while developing AI applications. CONCLUSIONS: The primary use of AI in medical education was for learning support mainly due to its ability to provide individualized feedback. Little emphasis was placed on curriculum review and assessment of students' learning due to the lack of digitalization and sensitive nature of examinations, respectively. Big data manipulation also warrants the need to ensure data integrity. Methodological improvements are required to increase AI adoption by addressing the technical difficulties of creating an AI application and using novel methods to assess the effectiveness of AI. To better integrate AI into the medical profession, measures should be taken to introduce AI into the medical school curriculum for medical professionals to better understand AI algorithms and maximize its use.
BACKGROUND: Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research. METHODS: This rapid scoping review, conducted over 16 weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape. RESULTS: The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education. CONCLUSION: The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework.
INTRODUCTION: Image segmentation in medical physics plays a vital role in image analysis to identify the affected tumour. The process of subdividing an image into its constituent parts that are homogeneous in feature is called Image segmentation, and this process concedes to extract some useful information. Numerous image segmentation techniques have been developed, and these techniques conquer different restrictions on conventional medical segmentation techniques. This paper presents a review of medical image segmentation techniques and statistical mechanics based on the novel method named as Lattice Boltzmann method (LBM). The beauty of LBM is to augment the computational speed in the process of medical image segmentation with an accuracy and specificity of more than 95% compared to traditional methods. As there is not much information on LBM in medical physics, it is intended to present a review of the research progress of LBM.OBJECTIVE: As there is no review paper on the research progress of the LB method, this paper presents a review with an objective to give some thought regarding the different segmentation for medical image and novel LB method to advance interest for future investigation and exploration in medical image segmentation.METHODS: This paper in attendance a short review of medical image segmentation techniques based on Thresholding, Region-based, Clustering, Edge detection, Model-based and the novel method Lattice Boltzmann method (LBM).CONCLUSION: In this paper, we outlined various segmentation techniques applied to medical images, emphasize that none of these problem areas has been acceptably settled, and all of the algorithms depicted are available for broad improvement. Since LBM has the benefits of speed and adaptability of modelling to guarantee excellent image processing quality with a reasonable amount of computer resources, we predict that this method will become a new research hotspot in image processing.