By: Rachel Weinberg-Rue,
Suicide has become the second leading cause of death among young adults aged 10-24 in the United States today. Over the past 30 years, suicide rates in the country have continued to rise. More young people die from suicide than from cancer, heart disease, AIDS, birth defects, stroke, pneumonia, influenza, and chronic lung disease combined. Society is well aware of the problem. There are many factors and behaviors that healthcare providers and loved ones can trained to pick up on. According to the statistics, four out of five teenagers who attempt suicide provide warning signs. However, the attempts happen anyway, which strongly indicates that the warning signs come far too late if they come at all. The reality of the matter is that suicide is not easy to diagnose. If it was, it would not be the crisis that it is today.
Developments have been made within federal law to address these concerns. Since the Call to Action to Prevent Suicide (1999), a national conference on suicide prevention, the government has been actively involved in creating national strategies of suicide prevention. Laws like the Garrett Lee Smith Memorial Act, which aimed to reduce suicide prevention among young adults and was signed into law in 2004, have been passed. The government has funded many research projects, and many suicide prevention programs have been proposed. However, the government’s efforts have clearly been unsuccessful given the climbing suicide rates.
Luckily, new scientific research suggests that it may be possible to detect suicidal tendencies in individuals with the use of brain scans and artificial intelligence. Researchers at Carnegie Mellon and the University of Pittsburgh analyzed how suicidal individuals think and feel differently about concepts like life and death by looking at how their brains reacted using fMRI data. They then programmed machines with learning algorithms to be able to detect frontal lobe flares at the mention of those concepts. 90 percent of the time, the computations enabled the machines to accurately and successfully pick out suicidal ideators. The machines were actually able to distinguish between people who had actually attempted self harm from those who only thought about it. Researchers hope to further develop these algorithms to search for clues that would indicate suicide-linked brain patterns before self-harm even occurs.
The downside of using fMRI imaging to detect suicidal ideators is the expensive costs involved, which is why other researchers have suggested that similar algorithms can be used to analyze medical records, social media, and data that is more accessible and available than brain scans. Researchers at Florida State and Vanderbilt have seen an 85 percent success rate using such algorithms.
The government has a legitimate interest in preventing suicide in our country and has been been increasingly involved in suicide prevention efforts, but more must be done. The development of suicide-prevention algorithms could play a major role in structuring the legal approach to suicide prevention. Hopefully, in the future, we will see laws that utilize the data gathered by algorithms like these and provide viable strategies that can be implemented in our communities.
 Youth Suicide Statistics, Tʜᴇ Jᴀsᴏɴ Fᴏᴜɴᴅᴀᴛɪᴏɴ, http://prp.jasonfoundation.com/facts/youth-suicide-statistics (last visited Nov. 2, 2017).
 Megan Molteni, Controversial Brain Imaging Uses AI to Take Aim at Suicide Prevention, Wɪʀᴇᴅ (Oct. 30, 2017, 12:00 PM), https://www.wired.com/story/fmri-ai-suicide-ideation.
 Supra note 1.
 Molteni, supra note 2.
 Vivian Le, Note, Fighting Against the Silent Epidemic: An Imperative for a Federal Suicide Prevention Act Narrowing the Lens on Mental Health, 25 S. Cᴀʟ. Rᴇᴠ. L. & Sᴏᴄɪᴀʟ Jᴜsᴛɪᴄᴇ 87, 99 (2015).
 Id. at 110.
 Id. at 101–02.
 Molteni, supra note 2.
 Molenti, supra note 2.
 See Id.
Image source: https://timedotcom.files.wordpress.com/2014/12/brain-scan-fmri-mri.jpg?quality=85.