Artificial Intelligence : This Artificial Intelligence can anticipate behavioral disorders: Duke researchers receive a $15 million federal grant to help identify at-risk teenagers.

Artificial Intelligence : A Duke University research team has earned a $15 million federal grant to enhance a predictive artificial intelligence model used to identify teenagers at risk of developing mental illness. The program, directed by Professor of Psychiatry Jonathan Posner, Assistant Professor of Biostatistics and Bioinformatics Matthew Engelhard, and AI Health Fellow Elliot Hill, is an important step toward proactive mental health care in the United States.

Artificial Intelligence : Predicting danger before symptoms appear

The Duke Predictive Model of Adolescent Mental Health (Duke-PMA) evaluates a variety of indicators to predict which teenagers are most likely to develop psychiatric disorders within a year. Unlike traditional techniques, which respond only when symptoms appear, the paradigm aims to transform psychiatry from reactive to preventive care. “In the way that psychiatry is currently practiced, it tends to be reactive, meaning we wait until someone’s developed a psychiatric illness, and then we institute treatment,” Posner told the agency.

Accuracy without expensive tests

The algorithm identified adolescents aged 10 to 15 at risk of major mental health difficulties with 84% accuracy, and it performed consistently across socioeconomic, racial, and gender lines. Notably, the tool depends merely on questionnaires rather than expensive imaging or laboratory tests, making it scalable and usable in a wide range of clinical settings.

Actionable insights for clinicians

Duke-PMA also identifies characteristics that doctors can immediately address, such as sleep patterns and family conflict, providing useful information for early intervention.

Posner said that clinicians might use the approach to evaluate patients during routine visits, receiving results that quantified risk and identified relevant factors.

Increasing access to underserved communities

The $15 million federal award marks a watershed moment in the project’s development. The next phase will include 2,000 teenagers from rural clinics in North Carolina, Minnesota, and North Dakota, areas with limited access to mental health care. Posner emphasized the potential influence in these areas, stating that an automated tool would be especially useful where specialized skills are rare.

Observational study design

The study will be conducted as an observational trial, with teenagers being assessed using Duke-PMA and families being contacted again after a year to see if the model’s predictions match real mental results.

Balancing creativity with caution

While AI in medicine frequently elicits both excitement and fear, the Duke team emphasizes careful integration. Hill and Engelhard emphasized that Duke-PMA is intended to augment, not replace, professional judgment, with stringent patient privacy safeguards. Engelhard told the Associated Press, “We’re quite serious about protecting patients’ privacy, both in the context of the current study and in the future. This is information between you and your healthcare professionals.”

The effectiveness of interdisciplinary collaboration

Posner emphasized the interdisciplinary aspect of the study as critical to its success. The Duke team hopes to develop tools that not only identify risk but also lead therapies that can change the trajectory of teenage mental health by merging skills in psychiatry, biostatistics, and AI.

Redefining mental health care in the future

The initiative exemplifies an emerging trend in medicine: employing artificial intelligence to identify dangers and prevent illness rather than reacting to symptoms. Duke-PMA provides a paradigm for students, clinicians, and politicians to integrate technology, data, and human judgment in ways that could reshape mental health treatment for future generations.

“We’re very serious about protecting patients’ privacy, both in the context of the study that we’re doing, as well as more broadly, going forward,” Engelhard told the audience. “And so this is information that would be between you and your care providers.”

This strategy aims to strike a balance between innovation and caution, improving care while keeping the importance of human presence during clinical judgment.

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