Sofie Bergstrand, Catherine Heddle, Montse Sabaté, Marta Mas
Artificial intelligence (AI) tools have already shown great promise in improving the workflows of key tasks and processes within medical writing, freeing up time for us humans to focus on those unique abilities AI cannot replace…yet. At the top of the list are critical thinking, analytical skills, emotional intelligence, and creativity. More than that, we can harness those abilities to collaborate in multidisciplinary, international teams to create innovative and apt solutions – an integral part of our daily work as medical writers, particularly in joint tasks such as co-authoring. Indeed, digital collaboration tools for project teams are abundant (e.g., Google Suites, Asana, and Microsoft Teams) and have transformed the way we work, especially now that remote work has become the norm. Nevertheless, collaborative technology using AI appears to be lagging slightly behind in the new wave of AI tools suitable for medical writing. With the application of emerging technologies and AI on the rise, the potential for automating the collaborative medical writing experience looks promising.
{"title":"Embracing artificial intelligence in medical writing: A new era of efficiency and collaboration","authors":"Sofie Bergstrand, Catherine Heddle, Montse Sabaté, Marta Mas","doi":"10.56012/iamc1709","DOIUrl":"https://doi.org/10.56012/iamc1709","url":null,"abstract":"Artificial intelligence (AI) tools have already shown great promise in improving the workflows of key tasks and processes within medical writing, freeing up time for us humans to focus on those unique abilities AI cannot replace…yet. At the top of the list are critical thinking, analytical skills, emotional intelligence, and creativity. More than that, we can harness those abilities to collaborate in multidisciplinary, international teams to create innovative and apt solutions – an integral part of our daily work as medical writers, particularly in joint tasks such as co-authoring. Indeed, digital collaboration tools for project teams are abundant (e.g., Google Suites, Asana, and Microsoft Teams) and have transformed the way we work, especially now that remote work has become the norm. Nevertheless, collaborative technology using AI appears to be lagging slightly behind in the new wave of AI tools suitable for medical writing. With the application of emerging technologies and AI on the rise, the potential for automating the collaborative medical writing experience looks promising.","PeriodicalId":37384,"journal":{"name":"Medical Writing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136099745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Clinical trials are becoming more complex and the efforts to optimise drug development are rapidly evolving. This Q&A gives a short overview of the strategies Bristol Myers Squibb implements to incorporate diversity into the clinical trial development process with the intent to enhance equity and inclusion for the diverse patient community that uses the treatments we develop.
{"title":"Diversity in clinical trials: It takes a village","authors":"Lorena Kuri, Cathy Florek, Jateh Major","doi":"10.56012/ugmu9329","DOIUrl":"https://doi.org/10.56012/ugmu9329","url":null,"abstract":"Clinical trials are becoming more complex and the efforts to optimise drug development are rapidly evolving. This Q&A gives a short overview of the strategies Bristol Myers Squibb implements to incorporate diversity into the clinical trial development process with the intent to enhance equity and inclusion for the diverse patient community that uses the treatments we develop.","PeriodicalId":37384,"journal":{"name":"Medical Writing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136099748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Environmental sustainability has gained increasing attention in the healthcare sector – and likewise, in medical journals. Historically, September 2021 marked an interesting event as over 200 medical journals conjointly published a call for emergency action to limit global temperature increase. Much has continued to happen since then, with major medical journals frequently publishing on the health effects of environmental change and growth of dedicated daughter journals such as the Lancet Planetary Health. Why has this interest been growing so rapidly and how might medical writers positively shape its development?
{"title":"Why environmental sustainability requires us to focus on our handprint – and write about it","authors":"Egid van Bree","doi":"10.56012/avhf1452","DOIUrl":"https://doi.org/10.56012/avhf1452","url":null,"abstract":"Environmental sustainability has gained increasing attention in the healthcare sector – and likewise, in medical journals. Historically, September 2021 marked an interesting event as over 200 medical journals conjointly published a call for emergency action to limit global temperature increase. Much has continued to happen since then, with major medical journals frequently publishing on the health effects of environmental change and growth of dedicated daughter journals such as the Lancet Planetary Health. Why has this interest been growing so rapidly and how might medical writers positively shape its development?","PeriodicalId":37384,"journal":{"name":"Medical Writing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136099749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Coordinated noncore sentence constituents are likely to be disrupted by unnecessary comma punctuation.
协调的非核心句子成分很可能被不必要的逗号标点打断。
{"title":"Syntactic punctuation distraction. Comma: Over-usage Part 2","authors":"Michael Lewis Schneir","doi":"10.56012/quci1726","DOIUrl":"https://doi.org/10.56012/quci1726","url":null,"abstract":"Coordinated noncore sentence constituents are likely to be disrupted by unnecessary comma punctuation.","PeriodicalId":37384,"journal":{"name":"Medical Writing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136099750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Plagiarism damages the biomedical academic publication domain. Artificial intelligence (AI) is a rising hope in academic plagiarism hunting. However, new AI-based tools are available online to assist with plagiarising! This article presents plagiarism throughout history, especially in medicine, and the promises of AI to detect a new type of plagiarism, namely Aigiarism. The danger of the above-mentioned AI-based services to help in paraphrasing copied texts is also highlighted, including some proposed solutions.
{"title":"AI-based plagiarism detectors: Plagiarism fighters or makers?","authors":"Valérie Lannoy","doi":"10.56012/ovnr4109","DOIUrl":"https://doi.org/10.56012/ovnr4109","url":null,"abstract":"Plagiarism damages the biomedical academic publication domain. Artificial intelligence (AI) is a rising hope in academic plagiarism hunting. However, new AI-based tools are available online to assist with plagiarising! This article presents plagiarism throughout history, especially in medicine, and the promises of AI to detect a new type of plagiarism, namely Aigiarism. The danger of the above-mentioned AI-based services to help in paraphrasing copied texts is also highlighted, including some proposed solutions.","PeriodicalId":37384,"journal":{"name":"Medical Writing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136099763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The implementation of the European Medical Device Regulation (EUMDR) has driven innovation in the digitalisation and the development of artificial intelligence (AI)- powered automations for regulatory writing. This article explores a selection of tools designed for device-related regulatory activities, high lighting their functionalities and use cases. The goals of the article are to demystify the role of AI in medical and regulatory writing, explain the process of developing AI-based automations, illustrate how these tools benefit medical writers, and most importantly enhance the readers’ skills in assessing such tools. The article discusses five automation tools: avasis, DistillerSR, Fern.ai, MedBoard, and Nested Knowledge, provid ing an overview of their features and benefits. The article concludes by emphasising that these automations address certain pain points faced during medical writing, yet they prioritise different features. By doing so, they empower users to improve data quality and streamline tasks in regulatory writing. Since there is no one-size-fits-all tool, the decision-making process is ultimately that of the user, not only on the type of tool to select but also on how best to leverage the software to optimise their technical documentation.
{"title":"Digital tools for the clinical evaluation of medical devices: A guide to empower regulatory writers","authors":"Azza Gramoun","doi":"10.56012/hmts9332","DOIUrl":"https://doi.org/10.56012/hmts9332","url":null,"abstract":"The implementation of the European Medical Device Regulation (EUMDR) has driven innovation in the digitalisation and the development of artificial intelligence (AI)- powered automations for regulatory writing. This article explores a selection of tools designed for device-related regulatory activities, high lighting their functionalities and use cases. The goals of the article are to demystify the role of AI in medical and regulatory writing, explain the process of developing AI-based automations, illustrate how these tools benefit medical writers, and most importantly enhance the readers’ skills in assessing such tools. The article discusses five automation tools: avasis, DistillerSR, Fern.ai, MedBoard, and Nested Knowledge, provid ing an overview of their features and benefits. The article concludes by emphasising that these automations address certain pain points faced during medical writing, yet they prioritise different features. By doing so, they empower users to improve data quality and streamline tasks in regulatory writing. Since there is no one-size-fits-all tool, the decision-making process is ultimately that of the user, not only on the type of tool to select but also on how best to leverage the software to optimise their technical documentation.","PeriodicalId":37384,"journal":{"name":"Medical Writing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136099770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Uri Kartoun (PhD in robotics, Ben Gurion University of the Negev, Israel) is a Staff Research Scientist and an IBM Master Inventor, co-developer of technologies such as MELD-Plus, EMRBots, Memory-memory (M2) Authentication, and Subpopulation-based Feature Selection. Prior to joining IBM Research in 2016, Kartoun worked at Microsoft Health Solutions Group and at Massachusetts General Hospital. EMWA Guest Editor Daniela Kamir, PhD, interviewed Kartoun about clinical risk assessment tools, organ transplant allocation disparities, and how the Model for End-Stage Liver Disease (MELD) score is used to allocate livers for transplantation. The conversation has been edited for brevity and clarity.
Uri Kartoun(以色列内盖夫本古里安大学机器人博士)是一名员工研究科学家和IBM Master Inventor，是MELD-Plus、EMRBots、内存-内存(M2)身份验证和基于亚种群的特征选择等技术的共同开发者。在2016年加入IBM Research之前，Kartoun曾在Microsoft Health Solutions Group和Massachusetts General Hospital工作。EMWA特邀编辑Daniela Kamir博士就临床风险评估工具、器官移植分配差异以及如何使用终末期肝病模型(MELD)评分来分配肝脏移植等问题采访了Kartoun。为简洁明了起见，以下对话经过了编辑。
{"title":"Essential principles towards improving clinical risk assessment tools: A conversation with Uri Kartoun, PhD","authors":"Daniela Kamir","doi":"10.56012/hivc3918","DOIUrl":"https://doi.org/10.56012/hivc3918","url":null,"abstract":"Uri Kartoun (PhD in robotics, Ben Gurion University of the Negev, Israel) is a Staff Research Scientist and an IBM Master Inventor, co-developer of technologies such as MELD-Plus, EMRBots, Memory-memory (M2) Authentication, and Subpopulation-based Feature Selection. Prior to joining IBM Research in 2016, Kartoun worked at Microsoft Health Solutions Group and at Massachusetts General Hospital. EMWA Guest Editor Daniela Kamir, PhD, interviewed Kartoun about clinical risk assessment tools, organ transplant allocation disparities, and how the Model for End-Stage Liver Disease (MELD) score is used to allocate livers for transplantation. The conversation has been edited for brevity and clarity.","PeriodicalId":37384,"journal":{"name":"Medical Writing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136099761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of artificial intelligence (AI) writing assistants in the healthcare industry is becoming increasingly prevalent. These tools can help medical writers to generate content more quickly and efficiently, but they also raise concerns about the accuracy and completeness of the information that is produced. This study investigated whether readers can distinguish between health-related texts written by humans and those generated by AI writing assistants. A survey of 164 respondents found that slightly more than half could correctly identify the source of the healthcare text. Differences between healthcare professionals and non-healthcare professionals were not statistically significant. Medical writers were better at recognising that a text had been written by an AI model than were non-medical writers (P<.05). These findings suggest that it is important for organisations to establish clear guidelines regarding the use of AI writing assistants in healthcare. The authors of health-related content should be required to identify whether their work has been completed by a human or an AI writer, and organisations should develop processes for evaluating the accuracy and completeness of AI-generated content. This study has several limitations, including the small sample size. However, the findings provide valuable insights into the need for organisations to develop clear guidelines for their use.
{"title":"Can readers spot the AI impostor in healthcare writing?","authors":"Natalie Bourré","doi":"10.56012/fwhk6920","DOIUrl":"https://doi.org/10.56012/fwhk6920","url":null,"abstract":"The use of artificial intelligence (AI) writing assistants in the healthcare industry is becoming increasingly prevalent. These tools can help medical writers to generate content more quickly and efficiently, but they also raise concerns about the accuracy and completeness of the information that is produced. This study investigated whether readers can distinguish between health-related texts written by humans and those generated by AI writing assistants. A survey of 164 respondents found that slightly more than half could correctly identify the source of the healthcare text. Differences between healthcare professionals and non-healthcare professionals were not statistically significant. Medical writers were better at recognising that a text had been written by an AI model than were non-medical writers (P<.05). These findings suggest that it is important for organisations to establish clear guidelines regarding the use of AI writing assistants in healthcare. The authors of health-related content should be required to identify whether their work has been completed by a human or an AI writer, and organisations should develop processes for evaluating the accuracy and completeness of AI-generated content. This study has several limitations, including the small sample size. However, the findings provide valuable insights into the need for organisations to develop clear guidelines for their use.","PeriodicalId":37384,"journal":{"name":"Medical Writing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136099765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When Shiri Diskin and Daniela Kamir suggested in 2021 to have a Medical Writing issue on automation in medical writing, little did I imagine how imperative this topic would be in 2023. I sincerely thank them for their avant-garde mindset and for producing this AI-some issue. Robots were supposedly created to perform the 3D tasks – the dirty (e.g., declogging sewage systems), the dangerous (e.g., defusing bombs), and the dull (e.g., drudgery of repetitive assembly work). At least that’s how it was for many years. More recently, robotics has been coupled with artificial intelligence (AI), and taking alliteration even further, more D’s have been added to their tasks, including the dear (i.e., expensive) and the difficult. These last two are distressing to many – will we soon be demoted, and eventually displaced?
{"title":"The D’s of robotics: Are we ready to delegate?","authors":"Raquel Billiones","doi":"10.56012/bzfs2718","DOIUrl":"https://doi.org/10.56012/bzfs2718","url":null,"abstract":"When Shiri Diskin and Daniela Kamir suggested in 2021 to have a Medical Writing issue on automation in medical writing, little did I imagine how imperative this topic would be in 2023. I sincerely thank them for their avant-garde mindset and for producing this AI-some issue. Robots were supposedly created to perform the 3D tasks – the dirty (e.g., declogging sewage systems), the dangerous (e.g., defusing bombs), and the dull (e.g., drudgery of repetitive assembly work). At least that’s how it was for many years. More recently, robotics has been coupled with artificial intelligence (AI), and taking alliteration even further, more D’s have been added to their tasks, including the dear (i.e., expensive) and the difficult. These last two are distressing to many – will we soon be demoted, and eventually displaced?","PeriodicalId":37384,"journal":{"name":"Medical Writing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136099752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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