Expectations for Artificial Intelligence (AI) in Psychiatry

Abstract

Purpose of Review Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine. Recent Findings For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. Summary The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.

Evidence, ethics and the promise of artificial intelligence in psychiatry

“…27 The notion that AI might catalyse a more reliable taxonomy of psychiatric disorders, as well as provide better predictions for people with-or who may develop-mental disorders is attractive. 1 Providing a technological explanation of something as aetiologically and socially complex as mental disorder provides a sense of objectivity and value neutrality. [28][29][30] Indeed, clinicians ought to apply the highest quality scientific evidence to support clinical decision-making.…”

“…Proponents suggest that leveraging big data (including genomics, demographic and environmental information) can improve access, diagnostic accuracy, guide prognostication, discover new treatments and provide more efficient and higher quality patient care. While research into the potential psychiatric applications of AI are in the nascent stage, 1 researchers are studying how electronic health records (EHR), rating scales, brain imaging data, social media platforms and sensor-based monitoring systems can be used to better predict, classify or prognosticate mental illnesses such as depression and psychosis, 2 3 or predict the risk of suicide. 4 Much has been written about the 'biomedical aspirations of psychiatry' 5 and the decades-long 'crises' of uncertainty regarding diagnosis, aetiology and treatment.…”

Challenges and Ethical Considerations to Successfully Implement Artificial Intelligence in Clinical Medicine and Neuroscience: a Narrative Review

“…The accuracy of the clinical AI prediction tools is tied to the training data where better quality data produces better quality predictions. The electronic medical records (EMR) and claims data that are routinely used as training data in medicine have quality problems related to inaccuracy, missing data, biases, coding errors, lack of diversity, unrepresentative samples, and lack of vendor software interoperability [21]. There are additional data quality concerns for psychiatry due to the high frequency of missing behavioral health data in the EMR [21,22].…”

“…The electronic medical records (EMR) and claims data that are routinely used as training data in medicine have quality problems related to inaccuracy, missing data, biases, coding errors, lack of diversity, unrepresentative samples, and lack of vendor software interoperability [21]. There are additional data quality concerns for psychiatry due to the high frequency of missing behavioral health data in the EMR [21,22]. Compared to nonmedical domains, the size of training data available for clinical AI prediction tools in psychiatry is much smaller [23], and a small training data size will decrease the accuracy of predictions [24,25].…”

“…A carefully considered, clearly defined process for implementing clinical AI technology in medical settings will improve the quality of the results. Despite the high expectations, there is a well documented productivity paradox, a delay in years between the adoption of a new technology and productivity increases, including in medicine [21]. The introduction of any new clinical technology, including AI prediction tools, will change the workflow, often in unexpected ways, and may result in new types of human errors and failure paths [36].…”