IN PARTNERSHIP


How could AI help medical writers? any generic langage models are able to create athentic content, bt they do not always perform well when the content or its frame of reference is new. This is a reslt of the training data sed becase langage models can only prodce content related to the data they have been trained on. A welldocmented downside of generic langage models is compter hallcinations, where a langage model maes p information or cites references when it has no information. This is obviosly a major concern for the field of scientific writing. To address this, some niche tools have been specifically trained to prodce content relating to scientific information. r own tool, Triloocs, combines a sectorspecific langage model with a core of epert rles to provide a set of giderails and only interprets relevant information from clinical trial data in relation to specific best practice criteria. It seems that the ftre of AI in the medical writing sphere may not be as stand-alone tools bt rather within platforms that se it in the contet of wider rles and other elements. sing AI tools in the medical writing space as


more of a walled garden maes sense becase of relctance to pload intellectal property, personal data, or other sensitive information to open platforms, where data ownership and data protection are crrently being debated. eglatory Athorities need to be confident in the accontability and traceability of raw data and docments spporting any claims.  eneral ata rotection eglation, protection of commercially sensitive information, and AI hallcinations remain major concerns. onetheless, langage models are ndobtably powerfl tools for creating athenticlooing tets from certain prompts, rewriting tets for different adiences e.g., in other langages, and prodcing simplified smmaries. ost medical writers wold be delighted to pass on rotine, mndane, and repetitive tass to a compter, which can do them more efficiently, accrately, and icly. This cold liberate writers to concentrate on the highly silled tass of contetalising and interpreting clinical data and allow them to have meaningfl data discssions with clinical teams mch earlier than is crrently possible.


What are the risks of AI? Data privacy is often the main risk that springs to mind. However, this is an inherent risk of any technology and not specific to AI. ome AI platforms present a risk of being internet- based. Also, open systems present a ris even in a nonAI contet. In or eperience with Triloocs, the ris of hman error has been significantly redced, if not eliminated. Important data that hmans may miss are identified by the tool, and we have not yet fond an isse raised dring ality assrance that was not already identified by the technology. The problem of AI hallcination is a case for real concern becase there is no room for false data, inferences, or references when dealing with clinical and scientific data. rom a medical writing perspective, a conservative approach is always best. r eperience is that it is better for the tool to highlight where something is missing or interpretations cannot be made, flagging data points for the medical writer to investigate rather than having a tool that prodces a complete bt misleading draft.


What does this mean for medical writers? One thing we always stress when talking abot or own tool, Triloocs, is that it does not replace the medical writer. It simply accelerates and enhances the writer’s ability to have meaningfl data discssions with the clinical team and speeds crafting of the report. ighly silled medical writers bring vale as critical thiners as they create stdy reports and related docmentation. e are still some way off from the ltimate goal of AI Artificial eneral Intelligence, which moves AI into the realm of hmanlie thoght. ntil that point, critical thining can only be done by hmans. In the short time that tools sch as hatT have captred or imagination, there is already an adage that describes where things cold be going in the short term AI might not tae yor job, bt someone who ses AI will. If we view AI as a tool that can spplement or wor, mae s more efficient and accrate, and relieve s of some of the heavy lifting, then it can become a powerfl resorce, freeing s to focs on the more valable wor of critical thining and crafting a strong narrative in or highly comple and vital wor.


Outsourcing In Clinical Trials | 9


References available upon request


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58  |  Page 59  |  Page 60  |  Page 61  |  Page 62  |  Page 63  |  Page 64  |  Page 65  |  Page 66  |  Page 67  |  Page 68  |  Page 69  |  Page 70  |  Page 71  |  Page 72