The legal industry is notorious for its high levels of work pressure and stress, often leading to burnout among legal professionals. Recognising the importance of maintaining mental wellbeing, more lawyers, in-house counsel, and general counsel are turning to innovative solutions to support their mental health. One such solution that holds promise is the integration of artificial intelligence technologies. AI has the potential to revolutionise the way mental health is addressed in the legal profession, offering both opportunities and challenges in equal measure.
The Growing Role of AI in Mental Healthcare
Clinicians, therapists, and researchers have increasingly come to recognise the power of AI in the provision of mental healthcare. AI is being used to diagnose conditions, develop therapies, and enable more personalised approaches and treatments. It offers a range of possibilities that can significantly impact the mental health landscape in the legal industry. I imagine most people can only foresee a future where AI will play an increasing more important and broader role in this field.
AI Therapists: A New Frontier
Currently, one of the ways AI is transforming mental healthcare is through the use of therapeutic chatbots. These chatbots provide support and advice to individuals seeking help for mental health issues. Users can communicate with these AI therapists, unloading their deepest and most personal feelings without fear of judgment or stigma. These chatbots can also identify keywords that may trigger a referral to a human mental healthcare professional, ensuring timely intervention.
This week the BBC reported on the exploding popularity of a ‘psychologist’ chatbot on the website character.ai which has not only been racking up questions but also praise from its users.
The Introduction of AI Wearables
In addition to chatbots, AI is also being integrated into wearables, which can interpret bodily signals using sensors and offer help when needed. For instance, Biobeat collects data on sleeping patterns, physical activity, and heart rate variations to assess the user’s mood and cognitive state. By comparing this data with aggregated and anonymised information from other users, predictive warnings can be generated to alert individuals when intervention may be necessary. This empowers users to make adjustments to their behaviour or seek assistance from healthcare services proactively.
Diagnosing and Predicting Mental Health Issues
AI also plays a crucial role in analysing various data sources to identify warning signs of mental health problems before they progress to an acute stage. Studies have shown that machine learning algorithms can predict and classify mental health problems, including suicidal thoughts, depression, and schizophrenia, with high accuracy. These algorithms analyse data from electronic health records, brain imaging, smartphone usage, social media, and more.
Furthermore, AI can predict cases where patients are more likely to respond to cognitive behavioural therapy (CBT) and, therefore, be less likely to require medication. This has the potential to greatly improve patient outcomes, as it reduces the need for potentially life-limiting medications. Deep learning techniques have been used to validate the effectiveness of CBT as a treatment method, minimising the reliance on medication for certain patients.
Enhancing Patient Compliance
Ensuring patient compliance with prescribed treatments is a significant challenge in mental health. AI can play a crucial role in predicting when patients are likely to slip into non-compliance and can issue reminders or alert healthcare providers for manual interventions. This can be done through various channels, including chatbots, SMS, automated telephone calls, and emails. AI algorithms can identify patterns of behaviour or occurrences in patients’ lives that might trigger non-compliance, enabling healthcare workers to develop strategies to counteract these obstacles effectively.
Personalised Treatments: Tailoring Care to the Individual
AI has the potential to revolutionise mental health treatments by enabling personalised care plans for individuals. By monitoring symptoms and reactions to treatment, AI can provide insights that help adjust individual treatment plans. Researchers at the University of California have used computer vision analysis of brain images to create personalised treatment plans for children suffering from schizophrenia. This research focuses on “explainable AI,” ensuring that the algorithms used are understandable by doctors who may not have expertise in AI.
Challenges in Implementing AI in Mental Healthcare
While AI holds immense promise in supporting mental health in the legal or professional industries, it also presents specific challenges that must be addressed. These challenges require close collaboration between AI researchers and healthcare professionals to ensure ethical, unbiased, and effective implementation.
Addressing AI Bias
One critical challenge is AI bias, which refers to inaccuracies or imbalances in the datasets used to train AI algorithms. Biased data can perpetuate unreliable predictions and even perpetuate social prejudice. For example, if mental health issues are more likely to go undiagnosed among certain ethnic groups with limited access to healthcare, algorithms relying on this data may also be less accurate in diagnosing those issues. To overcome AI bias, engineers and healthcare professionals must work together to implement checks and balances, eliminate biased data, and ensure the ethical use of AI in mental healthcare.
Subjectivity in Mental Health Diagnosis
Diagnosing mental health issues often requires subjective judgment from clinicians, as it involves assessing self-reported feelings and experiences rather than relying solely on medical test data. This subjectivity extends to machines that are tasked with making diagnoses. The reliance on patient-reported information, coupled with AI only being able to interpret the data it is fed as a LLM may miss cues which a real mental health professional would pick up on, either through their experience or human ‘sixth sense’. This could result in misdiagnosis, poor advice, missed intervention opportunities for the end-user.
Understanding the Impacts of AI in Mental Healthcare
While there is a growing body of evidence supporting the potential benefits of AI in mental healthcare, there are still significant gaps in understanding how AI is applied in this field. Existing AI healthcare applications must be thoroughly evaluated to identify and mitigate risks, including the potential for bias. Greater research and evaluation are needed to ensure that AI solutions are safe, effective, and capable of improving patient outcomes. Time will tell whether these AI solutions produce meaningful long term benefits for their users.
The Future of AI in Mental Healthcare
As the legal industry grapples with the increasing importance of mental health support, AI offers a range of possibilities to enhance mental healthcare in the profession. However, it is crucial to proceed with caution and prioritise the human element in the integration of AI solutions.
In conclusion, while the risks and challenges associated with AI in mental healthcare are significant, the potential benefits are equally profound. There are clearly risks associated with over-reliance on still-developing technology very much still in its infancy, particularly on a subject which is as complex and nuanced as mental health. However, the unparalleled convenience and ease of access to personalised answers and responses to individual circumstances (even if it’s not qualified expert advice) is clearly already being appreciated by many professionals. It’s also not a zero-sum game; if these AI chatbots are able to identify risk factors for a user and urge them to seek further help from a trained medical professional, it has a useful medical purpose.
Technologists, futurists and medical professionals may give their own opinions on what the future holds for AI supported therapy, but creating a new avenue for people to discuss mental health in confidence and access mental health services should surely be seen as a net positive for society. Personally, I am keen to see how this fascinating technology develops in future and see how it can benefit us all.
For further information, please contact:
Kwan Cheung, Pinsent Masons
kwan.cheung@pinsentmasons.com
