Machine learning (ML) has exciting potential for a range of uses in clinical trials. However, the hype around the term may raise expectations that ML does not have the ability to deliver. Ultimately, ML is a tool, and like any tool, its value depends on how well you understand and manage its strengths and weaknesses. After all, while a hammer is an effective tool for driving nails into planks, it’s not the best option if you need to wash your windows.
ML has some obvious advantages as a way to quickly evaluate large, complex datasets and provide users with a first read quickly. In some cases, machine learning models can even identify subtleties that humans might struggle to notice. A stable machine learning model consistently and reproducibly produces similar results. This can be both an advantage and a disadvantage.
ML can also be highly accurate, assuming the data used to train the ML model is accurate and meaningful. Image recognition ML models are widely used in radiology, yield excellent results, and can capture what even the most highly trained human eye misses.
This does not mean that ML is ready to replace clinicians’ judgment or take their jobs, but the results so far show that ML is a tool to: provide compelling evidence that it may be of value as enhancement their clinical judgment.
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Even as machine learning models become more sophisticated, the human factor continues to be important, as the insights that clinicians have built up over years of experience are lacking. As a result, subtle differences in one variable can cause the model to miss what is important (false negatives) or exaggerate what is not important (false positives).
There is no way to program all possible effects on available data and there will inevitably be factors missing from the dataset. As a result, external influences such as people moving during ECG acquisition, suboptimal electrode connections, or ambient electrical interference can introduce variability that ML cannot address. Furthermore, ML is unaware of whether the end user has made an error, such as entering the wrong patient ID, whereas ECG readings are unique, like fingerprints, so a skilled clinician can see You may recognize what the trace you are looking for does not match. They had seen it before from the same patient, prompting questions about who the trace actually belonged to.
In other words, machines aren’t always wrong, but they aren’t always right either. The best results are achieved when clinicians use ML to complement rather than replace their efforts.
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Clinicians who understand how to effectively implement ML in clinical trials can benefit from it working well. for example:
- ML tools extract language from dictionaries to automate interpretation and reduce the risk of typos.
- ML algorithms that produce accurate clinical interpretations can reduce the number of rereads required for clinical interpretations.
- ML also reduces clinical trial costs by enabling sponsors to translate results more quickly.
The value of ML will continue to grow as algorithms improve and computing power increases, but there is little reason to believe that it will replace human clinical surveillance. Ultimately, ML provides objectivity and reproducibility in clinical trials, but humans can provide subjectivity and knowledge about factors the program doesn’t take into account. Both are required. Also, ML’s ability to flag data inconsistencies may reduce workload, but these predictions should be validated.
There is no doubt that ML has incredible potential in clinical trials. Its ability to rapidly manage and analyze large volumes of complex data saves research sponsors money and improves results. However, it is unlikely to fully replace human clinicians for assessing clinical trial data due to the large number of variables and potential unknowns. Instead, experienced clinicians will continue to contribute their expertise and experience to further develop the ML platform, reducing repetitive and tedious tasks with high reliability and low variability and allowing users to perform more complex tasks. Allows you to concentrate.
Photo: Gerd Altmann, Pixabay
