How Artificial Intelligence Can Transform Randomized Controlled Trials

Abstract

With the advent of deep learning (DL), the application of artificial intelligence (AI) and big data in healthcare has started transforming the way we approach medicine including clinical trials. 1,2 The randomized controlled trial (RCT) has been traditionally accepted as the most robust method of assessing the risks and benefits of any intervention. 3 However, the undertaking of an RCT is not always feasible due to the rarity of the disease, or time and costs that would impinge on the healthcare system.AI is an academic discipline founded in 1956. 4 Machine learning (ML) is a subfield of AI that can learn complex relationships or patterns from data and make accurate decisions. 5 DL or deep artificial networks are a relatively new subfield of ML that takes advantage of powerful computational processing capacity provided by Graphic Processing Units and exponentially increasing datasets from medical records, images, multi-omics, and other "Big Data". 6 By feeding an enormous amount of data in training, a DL algorithm allows the model to alter its internal parameters between each neuronal layer to increase its performance. Applications of AI, DL in particular, have been successful in ophthalmic imaging research, 7-10 and the application of AI in RCTs may become reality in the near future.Common pitfalls of unsuccessful RCTs include poor patient selection, inadequate randomization with residual confounders, insufficient sample size, and poor selection of end points. 11 With well-curated large datasets that incorporate clinical and multimodal imaging, AI models can be trained to select the potential study participants without relying on costly manual review to predict the natural history of each study participants with advanced statistical methods, and to assess study end points in a data-driven method. Given these advantages, the application of AI has potentials for more efficient execution and greater statistical power than what would be expected from traditional RCTs.First, ML models can drastically improve the patient selection process, thus lowering the burden of individual screening and need for large sample sizes. Recruiting the patients who meet precise selection criteria is crucial to avoid potential confounders or misclassifications. ML can combine multimodal data, such as imaging, laboratory, and other complex -omics data, to screen and select patients who match complex inclusion criteria, which can improve the recruitment efficiency. This is one of the areas in which the American Academy of Ophthalmology's Intelligent Research in Sight (IRIS) data will be utilized for RCT recruitment (personal communication, Flora Lum, MD).In addition to the efficient selection process, having a sufficient sample size to enable detection of statistically significant differences between groups is critical. Many RCTs require a large sample size because the effect of the treatment in question is small. 12 AI has the potential in selecting "the ideal" patients for RCTs, who are "fast progressors" of the disease based on the AI'...

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