![\"\"](\"https:\/\/i0.wp.com\/thepublicationplan.com\/wp-content\/uploads\/2024\/06\/Banner-graphic.jpg?resize=1360%2C773&ssl=1\")
<\/p>\n<\/p>\n
The 12th <\/sup>European Medical Writers Association (EMWA<\/a>) symposium, entitled \u2018AI in Medical Writing\u2019<\/em> took place on 9 May. The symposium explored technological aspects of AI, ethical considerations, and showcased practical applications for medical writers and communications specialists. If you missed the morning session, you can catch up on the key themes with our summaries below, or get a quick refresher if you were in attendance!<\/p>\n Y<\/em>ou can read our summary of the afternoon session of the symposium here<\/a>.<\/em><\/strong><\/p>\n KEY TAKEAWAY<\/strong><\/p>\n Sepanta Fazaeli (Stryker) presented the opening session of the symposium on how natural language processing (NLP) models can be used to expedite the development of systematic medical reviews. NLP models have evolved from traditional machine learning models (with no semantic understanding) and deep learning models that use neural networks (with some semantic understanding), to large language models (LLMs) such as Open AI<\/a>\u2019s ChatGPT<\/a>, which offer powerful interpretation of text and the ability to better capture the nuances of human language.<\/p>\n NLP models have evolved as a powerful means for interpreting human language.<\/em><\/p>\n<\/blockquote>\n Fazaeli outlined a workflow for developing systematic reviews using AI:<\/p>\n There are now multiple tools offering automation of some or all of these steps, though none are necessarily validated. Writers should select tools based on what they want to achieve, and query the validation, metrics, explainability, and the specificity and sensitivity of a tool before purchase or use.<\/p>\n KEY TAKEAWAY<\/strong><\/p>\n Avi Staiman (Academic Language Experts and SciWriter.ai) provided recommendations on how writers can make the most of AI tools using their own subject knowledge. Staiman emphasised that LLMs need to be guided in an iterative manner to achieve the best output.<\/p>\n Effective prompting is key to obtain the desired results from LLMs. When prompting, the following elements can be used to tailor the output:<\/p>\n Staiman gave the following example of an effective prompt using some of these elements:<\/p>\n \u201cYou are a science writer [1]<\/strong> writing an article for the New England Journal of Medicine [3]<\/strong>. I want you to write an exhaustive literature review [2]<\/strong> on the topic of the main symptoms of colon cancer including gaps in the research. The literature review should focus on research published in the last few years [5]<\/strong>. Don\u2019t include an introduction or conclusion [6]<\/strong>.\u201d<\/em>\u00a0<\/strong><\/p>\n<\/blockquote>\n KEY TAKEAWAY<\/strong><\/p>\n Ashish Uppala (Scite.ai) discussed medical writing in the era of AI. The arrival of generative AI tools has enabled writers to delegate a greater proportion of cognitive load than ever before. However, the human writer is still responsible for the thinking process \u2013 this was true historically when words were written physically on clay and then using pen and paper, and is still true today.<\/p>\n Rather than using generative AI tools as automated agents, which might increase the risk of bias and error propagation, Uppala encouraged writers to leverage these tools as copilots to optimise efficiency, with documentation at each step of the process. To avoid unintentionally propagating errors in publications, medical writers should use tools such as Scite.ai<\/a> that \u2018show their work\u2019 by indicating the raw information used to generate an output. Uppala concluded his presentation by calling on medical writers to provide feedback to entrepreneurs to help improve generative AI tools for scientific publications.<\/p>\n The methods of written communication may have evolved, but humans are still responsible for the thinking process.<\/em><\/p>\n<\/blockquote>\n KEY TAKEAWAY<\/strong><\/p>\n Carlo Scollo Lavizzari\u2019s (specialist in intellectual property protection) talk focussed on intellectual property (IP) in the context of AI. As the use of AI tools becomes more routine, organisations may need to update their legal contracts and agreements. IP legislation for AI is a highly evolving landscape, so companies need to be aware of changes to the law. There are IP considerations throughout the process of using AI, from the inputs used to prompt AI (which may contain existing IP), to the resulting outputs (which could infringe upon existing IP or constitute new IP). Protection of AI tools themselves using IP is also a current topic, with an increasing numbers of patent applications for AI-related inventions.<\/p>\n Lavizzari made the following recommendations for individuals and companies when using AI tools:<\/p>\n KEY TAKEAWAY<\/strong><\/p>\n In his presentation, Victoriano Colodr\u00f3n (Copyright Clearance Center) provided an overview of the basic principles of copyright and how copyright and generative AI intersect. Both economic and moral rights are implicated in the training of AI technologies. LLMs, for example, are trained using vast amounts of copyrighted materials, and these materials are copied, stored, and recreated by the LLMs.<\/p>\n Generative AI tools can infringe on copyright in two main ways:<\/p>\n Although many countries have no specific AI-related laws and rely on existing statutes, a major development has been the recent approval of the EU AI Act, which emphasises that AI users need permission to utilise copyrighted work unless exceptions apply. Transparency is a key issue globally, and the EU AI Act will require generative AI providers to make a sufficiently detailed summary of copyrighted works used to train their systems publicly available, with a similar bill pending in the US<\/a>.<\/p>\n A key question is how AI providers can obtain permissions to use protected works in their systems. Rather than using direct licensing from individual rightsholders, Colodr\u00f3n recommended voluntary collective licensing, which involves the aggregation of rights from multiple rightsholders by a collective licensing organisation, which then collects royalties from users and distributes them to rightsholders. Collective licensing thus enables a faster and more convenient way for content users to gain access to rights.<\/p>\n Colodr\u00f3n emphasised that outputs from generative AI tools are improved by the use of high quality, responsibly sourced copyrighted works, which increase accuracy and reduce bias, and thus it is in everyone\u2019s best interests that AI is paired with respect for creators and copyright.<\/p>\n Generative AI tools can infringe on copyright by ingesting copyrighted materials and by producing output material that is identical or substantially similar to the original work.<\/em>\u00a0<\/strong><\/p>\n<\/blockquote>\n KEY TAKEAWAY<\/strong><\/p>\n Pawel Boguszewski (Nencki Institute of Experimental Biology) discussed the evolution of intelligence in biology and in artificial systems. Although there is no one clear definition of intelligence, there is a general agreement that it is based on the ability to learn and apply information. We now know that rather than being a reactive machine, the human brain is a predictive machine, which allows it to respond to the environment in real time. Neuroscientists are now trying to elucidate the parts of the brain responsible for predicting events.<\/p>\n Just as living intelligence has evolved over time, the concept of AI has progressed from the Turing test in the 1950s, through to today\u2019s LLMs. Many modern AI tools, for example Google DeepMind<\/a>\u2019s AlphaGo Zero<\/a>, and AlphaFold<\/a>, use artificial neural networks that were designed to mimic the living brain. Recent discussions<\/a> have gone as far as asking whether AI has gained consciousness. Boguszewski remarked that there are two competing schools of thought on the modern definition of consciousness<\/a>. The first (\u2018global workspace theories\u2019) defines mental states as being conscious when they are broadcast within a global workspace in which frontoparietal networks play a central hub-like role; using this definition, a machine could be built that is said to have consciousness. The second (\u2018integrated information theory\u2019) states that consciousness is identical to the cause-effect structure of a physical system that specifies a maximum of irreducible integrated information; using this latter definition, a machine cannot be conscious. Boguszewski concluded his talk by drawing the audience\u2019s attention to the impressive advancements that are taking place in both neuroscience and AI today, which may further improve understanding of our own intelligence.<\/p>\n Many modern AI tools<\/em> use artificial neural networks that were designed to mimic the living brain.<\/em><\/p>\n<\/blockquote>\n KEY TAKEAWAY<\/strong><\/p>\n In his talk, Mike Katell (The Alan Turing Institute) discussed ethics in the context of AI. Katell defined AI ethics as the set of tools for guiding responsible choices for the design, development, and deployment of digital technology. The SAFE-D<\/a> (Sustainability, Accountability, Fairness, Explanaibility, Data stewardship) principles, for example, serve as a starting point to reflect upon the possible harms and benefits associated with data-driven technologies.<\/p>\n Katell highlighted several key challenges when considering ethics in AI:<\/p>\n Key questions include who should be accountable if a system causes harm, and who should take responsibility for actions that cannot be explained \u2013 the AI company, the user, or the decision maker? Katell emphasised the need for caution around the claims of cost savings and enhanced capabilities made by AI tools in the long term, and highlighted some of the larger issues of AI at play, such as labour issues, the environmental costs of AI, and concentration of power by a small number of companies.<\/p>\n Users of AI technologies should be aware of the downsides of such tools from an ethical standpoint, in addition to the benefits that they bring.<\/em>\u00a0<\/strong><\/p>\n<\/blockquote>\n KEY TAKEAWAY<\/strong><\/p>\n Alison Rapley (Freelance Medical Writing Consultant) gave an overview of the ethical concerns that need to be considered in clinical research studies that utilise AI, such as studies that involve patient monitoring, or prediction or diagnosis of illness through digital health applications and platforms. Rapley identified the following potential issues:<\/p>\n Just because you can use AI, it doesn\u2019t mean you should.<\/em><\/p>\n<\/blockquote>\n KEY TAKEAWAY<\/strong><\/p>\n Uma Swaminathan (GSK) and Art Gertel (MedSciCom) co-presented a talk on AI from the perspectives of pharmaceutical companies and the general public. Swaminathan highlighted that patients, ethics, and trust should be at the centre of the pharmaceutical industry. AI can bring important benefits for patients, such as accelerated innovation and greater efficiency, and therefore faster approval of new treatments. However, these benefits need to be balanced with the risks, which include questions of accountability and explainability, and data privacy and data\/algorithm bias.<\/p>\n AI should be human-centric, with human accountability. Company policies should be updated to ensure that they are fit for purpose for AI, and there should be proactive risk management and robust governance in place. Decisions should be made collectively and collaboratively, rather than by individuals, to ensure ethical practice.<\/p>\n Gertel emphasised the importance of patient trust in the context of AI. Healthcare decisions are no longer being made solely by the physician: many patients are now taking on the role of partners in their care decisions, by consulting technologies such as Google, and now AI, for information. It is more important than ever that patients have access to trustworthy material supporting the principles that healthcare is safe, effective, patient-centred, timely, efficient, and equitable<\/a>.<\/p>\n It is more important than ever that patients have access to trustworthy material supporting the principles that healthcare is safe, effective, patient-centred, timely, efficient, and equitable.<\/em><\/p>\n<\/blockquote>\n KEY TAKEAWAY<\/strong><\/p>\n Mitchell Silva\u2019s (Esperity & Patient Centrics) talk focussed on AI from the patient\u2019s point of view. Silva noted that there are opportunities for generative AI tools to assist with patients\u2019 needs at all stages of their journey, from earlier detection of symptoms and accelerated diagnosis, to better disease understanding and optimised treatment decisions. For example, patients can upload their medical files to ChatGPT to obtain lay information, and deep fake avatars can be used by time-poor physicians to educate patients and answer their questions in the patient\u2019s language.<\/p>\n Silva urged caution regarding some of the potential negative effects of generative AI tools, namely data privacy, accuracy and reliability, and regulatory compliance (for example with General Data Protection Regulation).<\/p>\n Generative AI can assist patients with better understanding of their disease.<\/em><\/p>\n<\/blockquote>\n \u00a0<\/em>Why not read our summary of the afternoon session<\/a> of the symposium.<\/em><\/p>\n \u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2013<\/p>\n Written as part of a Media Partnership between EMWA and The Publication Plan, by <\/em>Aspire Scientific<\/em><\/a>, a proudly independent medical writing and communications agency that believes in putting people first.<\/em><\/p>\n \u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2013<\/p>\nHarnessing AI for efficient systematic reviews in medical publications<\/h3>\n
\n\n
\n![\"\"](\"https:\/\/i0.wp.com\/thepublicationplan.com\/wp-content\/uploads\/2024\/06\/AI-librarian.jpeg?resize=1360%2C907&ssl=1\")
<\/p>\n\n
Beyond the hype: 5 ways you can use your domain knowledge to supercharge your writing with AI<\/h3>\n
\n\n
\n\n
\n
From ink to code \u2013 the evolution of medical writing in the AI era<\/h3>\n
\n\n
\n![\"\"](\"https:\/\/i0.wp.com\/thepublicationplan.com\/wp-content\/uploads\/2024\/06\/tools-of-the-trade.jpeg?resize=1360%2C907&ssl=1\")
<\/p>\nAI and IP: ramification for vendor, provider and customer agreements<\/h3>\n
\n\n
\n\n
Copyright and artificial intelligence: an overview of how they intersect<\/h3>\n
\n\n
\n\n
![\"\"](\"https:\/\/i0.wp.com\/thepublicationplan.com\/wp-content\/uploads\/2024\/06\/copyright.jpeg?resize=1360%2C907&ssl=1\")
<\/p>\nNeurobiological roots of artificial intelligence<\/h3>\n
\n\n
\n![\"\"](\"https:\/\/i0.wp.com\/thepublicationplan.com\/wp-content\/uploads\/2024\/06\/neural-network.jpeg?resize=1360%2C762&ssl=1\")
<\/p>\nEthical considerations in AI-supported medical writing<\/h3>\n
\n\n
\n\n
![\"\"](\"https:\/\/i0.wp.com\/thepublicationplan.com\/wp-content\/uploads\/2024\/06\/Ethics.jpeg?resize=1360%2C838&ssl=1\")
<\/p>\nEthical challenges and considerations to implementing AI in healthcare; a Research Ethics Committee perspective<\/h3>\n
\n\n
\n\n
![\"\"](\"https:\/\/i0.wp.com\/thepublicationplan.com\/wp-content\/uploads\/2024\/06\/AI-Ethics.jpeg?resize=1360%2C762&ssl=1\")
<\/p>\nArtificial intelligence: pharma view<\/h3>\n
\n\n
\n![\"\"](\"https:\/\/i0.wp.com\/thepublicationplan.com\/wp-content\/uploads\/2024\/06\/shake-hands.jpeg?resize=1360%2C962&ssl=1\")
<\/p>\n\u00a0<\/strong>Patient perspective on generative AI<\/h3>\n
\n\n
\n![\"\"](\"https:\/\/i0.wp.com\/thepublicationplan.com\/wp-content\/uploads\/2024\/06\/Patient-POV.jpeg?resize=1360%2C765&ssl=1\")
<\/p>\n
![\"\"](\"https:\/\/i0.wp.com\/thepublicationplan.com\/wp-content\/uploads\/2023\/10\/Summary_new-byline.png?resize=1024%2C125&ssl=1\")
<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"