WORCESTER MEDICINE
A.I. In Medicine Artificial Intelligence in Mental Healthcare: A Story of Hope and Hazard
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The 17th Annual Louis A. Cottle Medical Education Conference, exploring “The Age of Artificial Intelligence in Medicine,” took place on October 18, 2023, at the Beechwood Hotel, Worcester. Organized by the Medical Education Committee of the Worcester District Medical Society, the event featured a dinner and presentations by keynote speaker Dr. Andrew Beam and panelists Dr. Larry Garber, Dr. Neil Marya, and Ricardo Poza, discussing the evolving landscape of AI in medicine.
Winter 2023
Rajendra Aldis, MD, MSCS Nicholas Carson, MD, FRCPC
he term artificial intelligence (AI) was coined at a computer science conference at Dartmouth College in 1956. Its use in healthcare began soon after, starting with rule-based systems that followed hard-coded instructions and then evolving to more advanced models that learn independently using data. Though the rate of adoption of AI in mental healthcare has been slower than in other medical specialties, its presence is growing. AI has the potential to address several challenges faced by mental healthcare. Providers today have at their fingertips an overwhelming amount of data. Electronic health records contain tens of thousands of data points and providers must consider an increasing number of them when making clinical decisions. However, there are limits to how many variables humans can take into consideration at once, and when that capacity is exceeded, we experience information overload. When compounded by stress, fatigue, and competing demands on attention, information overload can lead to errors [1]. One of the most critical decisions providers make is determining suicide risk. Suicide was among the top 9 leading causes of death for people ages 10-64 and remains the second leading cause of death for people ages 10-14 and 25-34 [2]. There are dozens of factors that must be considered when assessing suicide risk, and making accurate assessments is challenging [3]. Healthcare providers struggle to identify patients at risk: 54% of people who die by suicide were seen by a healthcare provider in the month before their death, but clinicians are typically no better than chance at estimating suicide risk [4]. Several AI models have been developed that can sort through large amounts of data to identify patients at risk for suicide. One model, which calculated a risk score using a combination of electronic health record data and patient report, performed better than clinician assessment alone [5]. With rigorous testing via randomized controlled trials, such models might eventually prove to be a cost-effective way to bring at-risk patients to the attention of busy providers [6]. In addition to augmenting clinical decision making, AI can also help advance our understanding of mental disorders. The mental health diagnostic categories used currently are heterogeneous; two patients with different sets of symptoms can receive the same diagnosis. There is also considerable overlap between mental disorders such that the same set of symptoms can result in different
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