Radical Changes in Psychiatric Diagnosis Are on the Horizon

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I believe this artificial intelligence is going to be our partner. If we misuse it, it will be a risk. If we use it right, it can be our partner.

                         —Masayoshi Son

Sadly, for many people current psychiatric and psychological approaches do not provide sufficient clarity or relief. Unlike many traditional “physical” illnesses, which are often better characterized (though many still defy understanding), psychiatric illness presents challenges to medicine because of the massive complexity of the brain and mind, making it historically difficulty to develop tools for assessment and treatment. Until relatively recently, diagnostic categories were based on clinical observation and statistical analysis of symptoms and treatment responses, making for an imperfect science. While this situation still holds true to a very significant extent, at the dawn of the 21st century we began to see the development of more powerful tools as a result of advances in assessment and computational analysis, giving hope for future improvement. By better understanding brain function in wellness and illness, and making biological connections to psychology and mind, the body-mind, physical-mental split is slowing being bridged.

The state of the art.

One of the limitations of current psychiatric diagnosis is that many conditions overlap. Anxiety, mood disturbances, fear, difficulty with concentration and memory, energy level changes, and a variety of other symptoms are shared across many diagnoses. At least 50 percent of patients receive more than one psychiatric diagnosis, which sometimes is the result of diagnostic murkiness and sometimes the result of actual co-occurring conditions.

People visiting more than one provider may be diagnosed differently, leading to confusion, straining trust, and complicating recovery planning. Developing more accurate models of mental health is imperative, given that anxiety and depression cause the greatest lost productivity and burden on function worldwide (e.g. World Health Organization), and current treatments generally are very effective for only 30 percent of patients. There is a clear need for more accurate diagnostic approaches, for developing valid biological tests (“biomarkers”) and for linking diagnosis with more effective treatments and treatment plans. While the current diagnostic nomenclature represents the best effort to date, new methods are becoming available for better understanding psychological health.

Approaching psychiatric diagnosis by using mathematical tools to search for consistent patterns inherent in clinical data promises advantages over conventional diagnostic approaches, which are subject to error from human bias (in spite of statistical analysis) and the risk of holding onto familiar categories in favor of more accurate approaches, People tend to resist change, and while newer is not always better, being open to thoughtfully novel approaches moves healthcare forward. Machine-learning is a powerful tool for looking at massive data sets and discovering useful patterns in data which other techniques miss. By using AI-type approaches, researchers can leverage computation power to see consistencies in how symptoms cut across received diagnostic categories to develop “transdiagnostic” perspectives. While this has to an extent been done with adolescents, machine-learning has not be applied to adult psychopathology.

Toward a transdiagnostic approach to adult psychiatry.

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