IN PARTNERSHIP
Artificial intelligence for medical writing, part two: transparency, flexibility and control
AI should be a partnership between person and machine, says Dr Barry Drees, senior partner at Trilogy Writing & Consulting.
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n an earlier article about artificial intelligence (AI) for medical writing1 I stressed that AI tools would assist,
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accelerate and augment the process of clinical documentation. I also argued that the key to the third aspect, augmentation, was the use and future acceptance of standard texts. This, of course, is going to be the hardest to implement and where most of the pushback occurs. Everyone is in favour of making the
preparation of clinical documents easier and faster, but when you start talking about standardising the text, the acceptance drops precipitously. In addition, many writers feel uncomfortable with a tool that seems to be taking their thinking away from them and produces a document that they don’t understand or feel connected to. So how do we address these challenges to bring the huge potential power of AI to the writing of clinical documents? I believe that the keys are transparency, flexibility and control. Let’s start with transparency. A common complaint about many AI systems is the ‘black box’ nature of how the system makes decisions. The AI is run on some data set and an answer is produced. But the reasons or logic behind the decision is not immediately clear; in fact, many AI programmers have to admit they have no idea exactly how the AI system came to its solution. This is an inherent weakness of ‘big data’ AI systems that have been developed by feeding thousands (in our case) of documents into the system and allowing it to look for commonalities. Although they can produce amazing and plausible results, one never really knows how the solution was achieved and thus what other possible solutions were passed over or ignored. Some medical writers I have spoken with say
they have tried AI tools and found them very unsatisfactory: you feed the protocol and data tables into the AI tool and some kind of output
10 | Outsourcing in Clinical Trials Handbook
results. But you have no idea what the thinking was that went into the decisions, so it’s very hard to know whether you agree with the decisions or not. However, unlike using big data, a rules-based approach (where the computer is instructed to follow specific rules) can avoid this problem in that the AI tool can be instructed to indicate in the standard text what was decided and how. For example, usually some sort of summarisation is used in the CSR text when describing a lengthy data table, like, for example, the list of treatment-emergent adverse events (TEAEs), which can be many pages long. This is frequently done by creating a small summary table within the text displaying only a selected portion of all of the events. However, this often leads the reader to wonder how the data were selected from the original TFL table. The events listed are usually chosen according to some kind of cut-off value in frequency (eg all events that occurred in at least 5% of participants) and an AI tool can be instructed to give the cut-off that was selected in the summarising table title and text, as in “The most common (>5% of all participants) TEAEs were as follows”. This can be used for all of the many poorly defined comparative words like “most common”, “well-balanced”, “relevant difference”, “slightly higher”, etc, that currently plague clinical study reports and about which the regulatory authorities frequently complain. Thus, transparency will be a critical aspect of any successful AI medical writing tool and can also improve the consistency of clinical documentation across studies. The great need for this is illustrated by a recent
experience I had with a submission team and a discussion about what to say about the subgroups in a study for a clinical study report. For safety, should the events be compared between the treatment groups within a subgroup or between the subgroup and its opposite (men versus women or elderly versus
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