Alan Turing first proposed the concept of using computers to simulate intelligent behaviour in 1950, marking the inception of Artificial Intelligence (AI) into the world. Beginning as a simple series of “if, then” rules, AI has advanced rapidly over the past decades, now approaching human-like cognitive capabilities [Kaul et al., 2020]. AI has become common-place in the modern era, but its application in the healthcare field dates back to the early 1970s with MYCIN, an AI program that helped identify treatments for blood infections [XSOLIS Insights, 2021]. Over the decades, AI systems have progressed to analyse complex algorithms and self-learn, paving the way for their clinical application. The late 2010s witnessed a transformative stage in AI as DeepQA technology emerged, leveraging natural language processing to analyse unstructured data and generate probable answers. This innovation successfully identified altered RNA-binding proteins associated with the disease amyotrophic lateral sclerosis (ALS), further highlighting the potential use of AI in healthcare [Kaul et al., 2020]. Risk assessment models, enhancing diagnostic accuracy and streamlining workflow efficiency are just a few examples of AI’s impact on healthcare.

While the early stages of AI development showed little crossover with the field of psychology, contemporary approaches reveal a wide range of possibilities for AI in mental health care. From chatbots creating more accessible psychological interventions, to automating administrative tasks and facilitating training of new clinicians, AI has diverse applications in clinical psychology. AI has also shown promise in predictive medicine, helping to identify potential mental health issues from patterns of behaviour and results of assessment tools [The Alan Turing Institute, 2019]. However, integrating AI into healthcare demands caution, as these tools have exhibited biases in their learning, particularly in relation to race [American Psychological Association, 2023].