The practice of medicine has always been both an art and a science. Technology has long been an integral facet of healthcare, optimizing patient care through innovation. However, the deployment of state of the art technologies in mental healthcare is often less widely understood or accepted.
The reality, though, is that technology can be as beneficial in mental health practices as in general medical practices. For example, artificial intelligence (AI) systems are increasingly being incorporated into various aspects of mental health care, from diagnosis and treatment planning to treatment implementation.
This article examines the use of AI in mental health practices, describing both its benefits and its limitations.
AI to Facilitate Diagnosis and Treatment Planning
Perhaps the most significant advantage of AI in mental healthcare is the utility of these systems in patient diagnosis and evidence-based treatment planning.
For example, through capabilities such as Natural Language Processing (NLP), AI systems are able to engage patients via chatbots. These chatbots “converse” with patients, asking questions and reacting to patient responses in order to elicit the information needed to triage patients and arrive at a preliminary diagnosis. This information can then be forwarded to the therapist or psychiatrist in advance of the patient’s person-to-person consultation.
The important role that AI systems play in triage, diagnosis, and treatment planning derives from the technology’s unparalleled capacity to analyze immense volumes of data. AI systems were designed to synthesize a wide variety of data, from patient histories to literally billions of Big Health Data points, often in mere seconds.
The result is faster and more accurate patient diagnosis without the need for human intervention. This means that clinicians spend less time researching the patient’s medical history and relevant medical studies in order to arrive at a reliable differential diagnosis. And that means more time spent in direct patient care.
In addition to achieving more accurate and more expeditious diagnosis, AI technologies can also be used to facilitate evidence-based and highly personalized patient care practices. For instance, AI systems may identify a potential adverse reaction risk to certain psychotherapeutic or pinpoint therapies that have proven especially beneficial for specific patient populations.
AI and Treatment Implementation
In addition to facilitating patient diagnosis and treatment planning, AI technologies can also be highly beneficial in the implementation of patient care protocols. For example, patients who have been diagnosed with a mood disorder may use a variety of AI-powered smartphone apps to help them assess their mood and respond in healthful ways.
For instance, cognitive behavioral therapy (CBT) apps can help patients identify negative thoughts and thought patterns and formulate more productive and affirming responses. The result is a form of evidence-based care known as “cognitive restructuring.”
Similarly, meditation and mindfulness apps can help patients who are experiencing stress and anxiety. These AI-based systems can lead users through guided meditation, therapeutic breathing, and other virtual care sessions in order to mitigate the mental health distress they’re facing.
The Limitations of AI
For all the vast potential of AI technologies in mental healthcare, these tools are by no means a panacea. Humans need humans. There are aspects of mental healthcare that simply can’t be outsourced to machines.
And this means that the ethical use of AI in mental health practices necessarily means that their deployment will be strategic, judicious, and limited. As has been shown, AI can be highly effective in diagnosis and treatment planning. AI-powered apps can even support patients in the implementation of prescribed treatment protocols, such as the use of CBT techniques for stress management and cognitive restructuring.
However, AI systems can’t replicate or replace the human element. Clinicians can provide the empathy, the intuitive understanding, and the compassion that patients need and technology can’t emulate.
It’s also important for clinicians to bear in mind that access to AI-technologies is not universal and therefore should not be taken as a given. For instance, these systems require access to a robust, secure, and high-speed network in order to function to their full potential. AI deployment is increasing in our modern world, but many clinical practices, as well as the patients they serve, may still lack adequate access or infrastructure to support these technologies.
Artificial intelligence (AI) technologies are playing an increasingly important role in mental healthcare. AI systems are optimizing patient care through more rapid and more accurate diagnosis, as well as the formulation of personalized, evidence-based treatment plans. This not only improves patient experience and outcomes, but it can also significantly relieve the burden on clinicians facing surging demand and a declining workforce. In addition, AI technologies are highly effective in facilitating treatment implementation. For example, AI-powered apps can help patients use prescribed CBT techniques effectively. However, for all the promise of AI in mental healthcare, it must necessarily be used as an adjunct solution, never as a replacement for direct human intervention and care.