The Future of Clinical Trials: A Shift from Artificial to Augmented to Applied Intelligence

The Present Situation Regarding Clinical Trials

Clinical trials, the bedrock of medical advancements, must embrace significant innovation. With global spending on these trials exceeding $50 billion now and predicted to reach over $85 billion by 2030, we must question the value we’re getting from this rapidly growing industry. What is the role of Artificial intelligence (AI) in clinical trials?  There are frequently chances to address chronic health disorders that are hampered by the time and costs associated with major trials. Despite the fact that there is a clear demand for improved evidence that is supplied in a more expedient manner through clinical trials that are efficient, the fundamental costs of the majority of clinical trials have not changed.

The Role of Artificial Intelligence in Clinical Trials

AI is reshaping both scientific research and medical practice. AI holds immense potential to transform clinical research, from improving human judgement to automating tasks. Manual transfer and verification of data account for a significant portion of clinical trial costs. These processes are ripe for automation. Therefore, the breakthrough in clinical trials likely requires the integration of AI. 

Natural Language Processing in Clinical Trials

In a recent study, authors investigated if Natural Language Processing (NLP) models could effectively classify heart failure hospitalisations. They found a high agreement level between the NLP model and the trial-adjudicated case outcomes. This finding suggests that using AI for event adjudication could save significant time and costs. However, they have not yet identified the requirements for calibration, validation, and periodic performance assessment during the scaling process.

The Prospects for Artificial Intelligence in Clinical Trials

AI can significantly improve the development, execution, and dissemination of clinical trials. It can enhance efficiency and remove bias in areas like participant identification, study procedure generation, and trial material production. However, we need an oversight structure to ensure the trials don’t incorporate previous biases. By addressing the risks properly, we can safely and ethically realise the benefits of artificial intelligence in clinical trials. 

In conclusion, recent studies show that AI-enhanced clinical research is achievable and potentially beneficial. As we learn more, we must find ways to honestly communicate our limitations and failures without damaging the process’s integrity. High-quality evaluations of AI tools, using rigorous designs and analysis, are crucial to provide quality information about the benefits and risks of using AI in clinical trials. We need these kinds of publications to support the research behind AI-enhanced clinical trials and maintain trust in AI applications for these trials.