Frontiers in Psychiatry · 2018 · 54 citations · 15 references
ComputingComputer ScienceHealth InformaticsArtificial IntelligenceMedical EthicsSuicide PreventionEthical AssessmentEthics Of Automated Decision-makingRisk PredictionRisk ScoringSuicide Risk AssessmentHuman IntelligencePredictors Of Suicide RiskSuicide Prediction ModelsSuicide Prevention EthicsSuicide Risk Assessment EthicsSuicide Risk DetectionSuicide Risk Recognition
Suicide rates in the U.S. have risen 24 % over the past two decades, and identifying risk at point‑of‑care is difficult, leading to increasing use of risk scoring and AI. The study aims to address ethical concerns in suicide risk prediction by offering guidance on communication, consent, and controls.
In the United States, suicide increased by 24% in the past twenty years, and suicide risk identification at point-of-care remains a cornerstone of the effort to curb this epidemic.1 As risk identification is difficult because of symptom under-reporting, timing, or lack of screening, healthcare systems rely increasingly on risk scoring and now artificial intelligence (AI) to assess risk. AI remains the science of solving problems and accomplishing tasks, through automated or computational means, that normally require human intelligence. This science is decades-old and includes traditional predictive statistics and machine learning. Only in the last few years has it been applied rigorously in suicide risk prediction and prevention. Applying AI in this context raises significant ethical concern, particularly in balancing beneficence and respecting personal autonomy. To navigate the ethical issues raised by suicide risk prediction, we provide recommendations in three areas – communication, consent, and controls – for both providers and researchers.2
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