IN PARTNERSHIP
non-elderly)? The best choice is the former, as the latter will just show events typical for the subgroup (more pregnancies in the women, more deaths in the elderly, etc). Standardised comparisons noting exactly what was done should eliminate the need for these discussions. This brings us to the next key: flexibility. Another common complaint about AI systems based on big data is that they are often very dependent on the format of the data used to train the tool. There are cases where an AI tool trained for radiological diagnosis on one set of X-rays was then unable to be used by other clinics that used different X-ray machines and where the output had minor differences2
. This problem led one
radiologist to state, “I would want a human physician no matter what – even if a machine hums alongside.”3
Training and creating an AI
tool when all of the data is in an identical format is relatively straightforward; the trouble arises when the form of the output changes (sometimes for reasons totally unrelated to the AI tool). It is tempting for companies developing an AI tool for their internal use to design it specifically for their formats, but this can make it of very limited usefulness over time or across organisations. In contrast, not being a large pharmaceutical
company that could demand a consistent format but rather a service organisation with multiple clients, my company, Trilogy Writing & Consulting, did not have this luxury and wanted our tool to be as flexible as possible. As we would discover, this would present some serious programming challenges in dealing with different templates, data inputs and document styles. This was in addition to the flexibility necessary for the full range of clinical documents (just in terms of CSRs, we wanted to be able to handle phases I, II and III; comparative and non-comparative studies; and pivotal efficacy, massive safety and small first-in-human studies). Such flexibility is definitely a challenge in developing an AI tool, but if the goal is for AI to be truly transformational, then I believe it will be necessary. The final key is control. Unfortunately, the
word ‘control’ has acquired a rather unsavoury reputation of late, and many people immediately think of its abuse (and concepts like ‘control freak’, for example). In this case, however, I again would refer to the feedback that I have received from medical writers working with early versions of AI for clinical documentation. Many were
frustrated by the fact that after running the tool they felt they had lost control of what was produced. In subsequent review cycles, they felt it was difficult to discuss the draft with clients or team members, as they were forced to defend writing or data selection decisions with which they disagreed. Thus, a useful AI tool will need to be interactive with the writer, allowing them to make decisions about the document before or as it is created as well as being able to easily edit the document that is produced to avoid the “It would have been easier if I had just done it myself from the beginning” sentiment common to many writers using current AI tools. This is one of the biggest challenges facing
any AI tool, in medical writing or elsewhere: acceptance of the tool by the people currently employed in whatever activity the tool performs. In medical writing, this is particularly true, as writers are encouraged to feel ownership of the documents they produce to ensure quality. Giving the writer a measure of input and control into the process will be essential for overcoming the acceptance problem. It also improves the final product, for, while there are things that computers can do so much better than people in generating text, there are other things that are just simpler for people, who tend to be better at ‘fuzzy logic’ than computers. The true potential of AI will not be with an exclusively computerised process, but rather a hybrid process – a partnership between person and machine. Bringing AI abilities to the writing of clinical documents will not be like flicking a switch; it will require attention in the design and use of the AI tool to make it transparent, flexible and without losing control, in order to help make the approval process for new medicines faster and more transparent. There is great potential here, but it will require attention to the process and integration of the machine and the medical writer for the optimal outcome.
References 1.
https://content.yudu.com/web/442ay/0A447nd/OCTH004/html/
index.html?page=6&origin=reader 2. Allen, B, A Road Map for Translational Research on Artificial Intelligence in Medical Imaging: from the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop. JACR 2019; 16: P1179-1189 (
doi.org/10.1016/j.jacr.2019.04.014). 3. Couzin-Frankel J, Artificial intelligence could revolutionize medical care. But don’t trust it to read your X-ray just yet. Science 2019; (doi: 10.1126/science.aay4197).
To find out more about Trilogy Writing & Consulting, visit
www.trilogywriting.com
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