Background Opioids are strong pain medications that can be essential for acute pain. However, opioids are also commonly used for chronic conditions and illicitly where there are well-recognised concerns about the balance of their benefits and harms. Technologies using artificial intelligence (AI) are being developed to examine and optimise the use of opioids. Yet, this research has not been synthesised to determine the types of AI models being developed and the application of these models.
Methods We aimed to synthesise studies exploring the use of AI in people taking opioids. We searched three databases: the Cochrane Database of Systematic Reviews, Embase and Medline on 4 January 2021. Studies were included if they were published after 2010, conducted in a real-life community setting involving humans and used AI to understand opioid use. Data on the types and applications of AI models were extracted and descriptively analysed.
Results Eighty-one articles were included in our review, representing over 5.3 million participants and 14.6 million social media posts. Most (93%) studies were conducted in the USA. The types of AI technologies included natural language processing (46%) and a range of machine learning algorithms, the most common being random forest algorithms (36%). AI was predominately applied for the surveillance and monitoring of opioids (46%), followed by risk prediction (42%), pain management (10%) and patient support (2%). Few of the AI models were ready for adoption, with most (62%) being in preliminary stages.
Conclusions Many AI models are being developed and applied to understand opioid use. However, there is a need for these AI technologies to be externally validated and robustly evaluated to determine whether they can improve the use and safety of opioids.
- Addiction Medicine
- Pain Management
- Substance-Related Disorders
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Twitter @SeemaGadhia, @Richards_G_C
Contributors JR developed idea with SG. AG ran database searches. SG screened studies, extracted data, created the tables and figures and drafted the first manuscript. GCR extracted data on study designs and analysed data. All authors read and contributed to writing the manuscript.
Funding No funding was obtained for this study
Competing interests GCR was financially supported by the National Institute of Health Research School for Primary Care Research, the Naji Foundation and the Rotary Foundation to study for a doctor of philosophy (DPhil/PhD) at the University of Oxford (2017–2020). GCR is an associate editor for BMJ Evidence Based Medicine.
Provenance and peer review Not commissioned; externally peer reviewed.
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