Artificial intelligence (AI) has the potential to revolutionize how doctors approach and treat mental health. With AI, mental health professionals can access customized tools immediately, reducing their workloads and providing timely support. Keep reading to learn how AI can diagnose and provide ongoing support in the treatment of mental health issues.
Diagnosis
AI can predict and classify various mental health illnesses, including depression and schizophrenia. For example, 28 scientific studies showed how Artificial Intelligence can improve how psychiatrists diagnose mental illnesses in patients by utilizing electronic health records, mood rating scales, brain imaging data, and digital monitoring systems such as smartphones and video.
As AI technology advances, mental health practitioners may be able to define mental illnesses objectively, allowing for earlier detection and more personalized treatment. However, as AI is being integrated into mental health clinical care, it is important to exercise caution when interpreting these preliminary findings.
Here are a few helpful ways AI is being used to treat mental illnesses that you can start using today.
Treatment Methods
Artificial intelligence (AI) assists in efficient ways to support mental health treatment and improve access to care. Some examples of how AI improves mental health treatment include:
- Virtual chat bots
- Machine learning to analyze patterns and provide resources
- Personalized treatment plans
Virtual chat bots
Virtual therapists or mental health chatbots are one way AI supports mental health. By using machine learning and natural language processing, these virtual assistants comprehend user input and provide support and guidance to those struggling with mental health issues. People can access them through a variety of platforms, including websites, mobile apps, and social media, and they can provide convenient and confidential support.
There are several mental health chatbot apps available that offer various types of support and resources to users. Some examples include:
Woebot uses cognitive-behavioural therapy (CBT) techniques to help users identify and overcome limiting beliefs. It provides daily check-ins, mood tracking, and personalized feedback to help users change negative thought patterns.
Moodfit improves mental well-being through a combination of CBT, positive psychology, and mindfulness techniques. Users can build healthy habits and cope with stress with daily challenges, mood tracking, and personalized recommendations.
Ginger connects users with licensed therapists, coaches, and peer support specialists through messaging and video calls. The app offers support for various mental health issues, including depression, anxiety, stress, and relationship problems.
Machine learning
AI also analyzes large amounts of data in order to identify patterns relevant to bettering one’s mental health. For example, all the above apps use machine learning to identify daily check-ins on recurring moods to provide recommendations.
Machine learning algorithms can also analyze social media posts or other online activity to identify individuals who may be at risk of developing mental health problems. This data can be used to connect susceptible individuals with helpful resources.
Personalized treatment plans
Another area where AI is applied in mental health is in the development of personalized treatment plans. AI algorithms can identify the most effective treatment options for individuals based on their personal and medical histories. This identification process can help improve the effectiveness of treatment and save the time and resources needed to find the right treatment plan.
For example, a personalized treatment plan can include precision medicine that takes into account an individual’s unique symptoms, history, and other factors that may influence their mental health. This plan can also involve the use of multiple treatment options, such as medication, therapy, and lifestyle changes, to address the specific needs of the individual.
As AI becomes more prevalent in mental health, some challenges will remain to be addressed, including bias, privacy, and the need for human oversight and interpretation. However, it can significantly improve the way medical healthcare professionals approach and treat mental illness.
