In partnership with Trilogy Writing & Consulting AI for medical writers –
he term AI is broad. Different 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.
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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 example, 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”. It is 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 accurate but difficult to explain. This is one of the areas that makes some groups pause because they often show emergent behaviours not predicted by humans and can be unsettling, adding to concerns that AI is out of control.
Regulatory
friend or foe?
Artifi cial Intelligence (AI) is beginning to affect almost every industry, and medical writing is no different. Should medical writers be happy and embrace the technology, or should we resist as much as we can, assuming that we will all be replaced by machines? Lisa Chamberlain James, senior partner at Trilogy Writing & Consulting and Jamie Norman, chief product offi cer at TriloDocs discuss.
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 computational power provided by deep learning models.
Where does ChatGPT 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 modelling 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
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