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Artificial intelligence (AI) is constantly in the headlines, and we can’t stop sharing applications of how it has dramatically improved productivity. At work, slides, images and mostly writing of research have been using AI to assist with combing the internet for materials. And there is no hiding here because I did use AI to come about this topic and its research too.

The access and speed provided with AI is alarming. By analyzing user input in real-time, AI applications can be used to detect signs of deterioration in mental health and alert clinicians or provide immediate suggestions for coping strategies, promoting proactive and personalized mental health management and highlighting the shift towards technology-aided therapeutic treatment.

In the use of AI, we often encounter the most widely discussed challenges, data privacy and security. With AI tools processing sensitive patient information, ensuring the confidentiality and safe handling of patients’ data is of utmost importance.

Unauthorized access or potential data breaches can jeopardize patient trust and expose vulnerable individuals to risks. Moreover, as technologies and its ecosystem of applications evolve, ensuring informed consent and addressing algorithmic biases must be prioritized to maintain ethical integrity in mental health interventions, highlighting the necessity for robust regulatory frameworks.

But something just keeps popping in my mind. . .

If machines were advising humans on their mental health, what makes of human’s actual mental health in future?

Current use of AI technologies are designed to augment clinical practices by streamlining administrative processes such as scheduling and documentation, thereby reducing clinician administrative tasks, and allowing more time for patient interaction.

This seems ideal, to allow for more human interaction time.

AI tools such as digital therapeutics can also provide innovative means to deliver therapy outside traditional settings, allowing patients to engage with their mental health treatment through applications that tailor approaches based on individual needs and responses.

And this is something we have already seen in use of applications.

But the part where AI plays the role by supporting clinicians with predictive power and the ability to analyze datasets quickly in relation to early detection of mental health conditions or crafting personalized treatment plans, might seem too overly reliant on the part of data and machines to treat human conditions.

Ethical Challenges

Data privacy emerges as one of the foremost ethical challenges in the use of AI in mental health care. Given the sensitive nature of patient information, AI systems must incorporate stringent safeguards to protect against unauthorized access and data breaches. The implications of compromised data can be severe, ranging from individual privacy violations to potential exploitation of sensitive health data for profit. Healthcare providers must no longer be content with just treating their patients to recovery, but now ensure clients are informed about how their data is collected, stored, and utilized, thereby reinforcing trust in AI applications while safeguarding mental health treatment.

In the most common of AI challenges that result in the inability for that human touch, is most likely the problem with data poisoning or hallucinations.

Bias in AI systems constitutes a critical ethical concern, particularly in the realm of mental health diagnostics and treatment. AI algorithms, which are often trained on large datasets, may inadvertently embody inherent biases related to race, gender, and socio-economic factors. This risk of bias can perpetuate disparities in mental health care, leading to inadequate or ineffective treatment for marginalized populations. To mitigate this, developers and clinicians must engage in rigorous bias audits and refine AI models, ensuring equity and fairness in treatment outcomes across diverse patient groups.

Interestingly, the topic of correctly diagnosing mental health issues, go into the area of “precision treatment“, with better data from a combination of conditions. These could be in future used in different areas of AI therapist work.

So what is your take?

Would you want to be talking to a digital platform to assist with your mental health issues? What make you want to trust a bot to diagnose if you have mental health issues?