IN PARTNERSHIP AI for medical writers – A


rticial Intelligence I is aecting almost every industry, and medical writing is no dierent. Sould medical


writers emrace I or resist it assuming we will all e relaced y macines


How AI works in a writing context The term AI is very broad. ifferent branches of it often get conflated, but there are disciplines within the discipline. At its highest level, AI is a catch-all term for any computational technique that enables machines to mimic human behaviour. This could be as simple as a macro in Excel that automatically performs a set of calculations or procedures or as advanced as a facial recognition algorithm. The next layer of detail is referred to as


“machine learning”, which is a subset of AI that uses statistical methods to improve a model based on experience. For image recognition, this could be a system that improves the accuracy of recognising a certain animal under increasingly ambiguous scenarios. The next deeper level is so-called “deep learning”, a subset within machine learning, where a neural network is used to make connections. Incredibly large, multi-layered networks create computational systems that work more like the human brain. Many deep learning algorithms are actually closer to “black box systems”, in which the outcomes may be incredibly accrate bt difficlt to eplain. This can make some groups pause because they often show emergent behaviours that were not predicted by humans and can be unsettling, adding to concerns that AI is out of control. This is where the notion of “explainable AI” comes in. Being able to reverse-engineer outcomes and explain the results of AI models creates a more comforting outcome, although this may mean sacrificing some of the comptational power provided by deep learning models.


8 | Outsourcing In Clinical Trials


friend or foe? Lisa Chamberlain James, senior partner at Trilogy Writing & Consulting, and Jamie Norman, chief prodct officer at Triloocs, discss the current state of the art of AI in medical writing


r do at fit in ChatGPT uses neural nets to support the computation power of its outcomes. As a large language model, it retains a degree of “explainability”. Large language models generally use statistical models. In simple terms, a language model uses a set of training data to create a probability of the next word or series of words in a sentence. ChatGPT’s power comes from access to perhaps the largest corpus of training data of any language model. However, even ChatGPT has shown emergent behaviours. For example, it can be used to solve maths problems, which it was not specifically designed for, and although it can “solve” maths problems, it cannot interpret statistics. Language modeling also cannot assign probabilities to linguistically valid sequences that may not have been in the training data. This is a positive in the sense that it can create novel texts, but it also can produce results that are grammatically correct but factually incorrect. In this way, language models differ from cognitive models, which are closer to our own abilities to solve problems. The challenge of interpreting new concepts is an important consideration for AI. This is a reflection of the language model’s


inability to reason in the same way as a human – to make deductions from premises or to process insights rather than make probabilistic inferences from word frequencies. This explains why making new inferences from data can be challenging, and it is exactly the kind of challenge we face in interpreting statistical data from new drugs. The margins for error in this contet are significantly smaller so we cannot rely on language models alone. ChatGPT is a tool, and as any tool, it is only as good as the person using it. It is incredibly powerful, but to build products around it, its underlying working models, nuances, and other details must be understood.


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