Ethics

How to Ethically Integrate Artificial Intelligence in Clinical Practice

This article explores the many practical applications of artificial intelligence (AI) in mental health care, including important ethical considerations.

By Mental Health Academy

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This article explores the many practical applications of artificial intelligence in mental health care, including important ethical considerations.  

Related articles: Artificial Intelligence in Mental Health: Present, Promise and Peril, Clinician Perspective: Artificial Intelligence, Trauma and Mental Health, Ethical Decision-Making in Mental Health.

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Introduction

Artificial intelligence (AI) has become an integral part of many industries, including healthcare, with significant implications for mental health clinical practice. From aiding in diagnosis to offering ongoing therapeutic support, AI presents both opportunities and challenges (some of which have been discussed in this article). For mental health professionals, the critical task lies in harnessing these advancements ethically while ensuring patient care remains the primary focus.

This article explores the practical applications of AI in mental health, ethical considerations, and tools to integrate AI responsibly into clinical work.

Applications of artificial intelligence in clinical practice

Artificial intelligence offers powerful tools to enhance the effectiveness of mental health care. Below, we examine some of its most impactful applications, including diagnostic support, personalised treatment planning, chatbots and virtual assistants, clinical notetaking, and monitoring and predictive analytics.

Diagnostic support

AI has proven its ability to identify patterns in vast amounts of data, offering clinicians enhanced diagnostic accuracy. By analysing electronic health records (EHRs), genetic information, or neuroimaging data, AI algorithms can detect mental health conditions earlier and more precisely.

Example: A study by Graham et al. (2019) highlighted AI’s ability to predict suicide risk. By evaluating patient demographics, history, and social media activity, these models can identify high-risk individuals, allowing clinicians to intervene before a crisis occurs.

A clinical scenario: A clinician treating a 30-year-old patient experiencing depression incorporates an AI system that flags potential bipolar disorder based on sleep patterns, mood changes, and family history. This prompts further assessments, leading to an accurate diagnosis and tailored treatment.

Personalised treatment planning

AI’s ability to analyse data from thousands of cases enables it to predict how individual patients might respond to specific interventions. This ensures that treatment plans are more closely aligned with patients’ needs, minimising trial-and-error approaches.

Example: Research by Chekroud et al. (2021) demonstrated AI’s ability to match patients with treatments based on their unique profiles, improving outcomes significantly.

A clinical scenario: A patient with treatment-resistant anxiety sees little improvement after several rounds of therapy and medication. An AI platform recommends a blend of cognitive-behavioural therapy (CBT) and mindfulness techniques, based on similar cases in its database. The tailored plan proves more effective, restoring the patient’s quality of life.

Tools to explore: The AI-powered app Ada suggests potential treatment approaches by synthesising medical history and current symptoms into actionable insights.

Chatbots and virtual assistants

AI chatbots are increasingly used as adjuncts to therapy. These systems can deliver therapeutic interventions, monitor mood, and provide psychoeducation, all in real-time. While not substitutes for clinicians, they offer immediate support for individuals with mild to moderate mental health concerns.

Example: Fulmer et al. (2018) demonstrated that Tess, an AI-driven chatbot, significantly reduced symptoms of depression and anxiety by delivering CBT techniques to users.

A clinical scenario: A university student overwhelmed by academic stress engages with Woebot, an AI chatbot offering CBT-based support. The bot guides the student through exercises, tracks their mood, and suggests relaxation techniques, complementing ongoing therapy sessions.

Tools to explore: Woebot, Elomia and Youper are some examples of AI apps that offer evidence-based therapeutic interventions and psychoeducation.

Clinical notetaking

AI-drive notetaking is a popular, relatively new solution that helps streamline notetaking and reduce the administrative burden. This allows you, as a clinician, to dedicate more time and resources (including your attention!) to supporting clients.

For a more comprehensive take on notetaking, read Notetaking for Therapists: Best Practices and Innovations. Below are examples of AI-enabled notetaking tools:

  • Upheal: AI‑generated notes for therapists, psychiatrists, and coaches.
  • Heidi AI: A voice-enabled scribe system designed for clinicians.
  • Suki AI: A voice-enabled digital assistant that helps clinicians create accurate, compliant notes quickly.
  • Nirvana AI: An AI-based service that listens to therapy sessions and summarises key points, freeing up therapists to focus on client engagement.

Monitoring and predictive analytics

Continuous monitoring through smartphones and wearable devices has revolutionised mental health care. AI analyses behavioural and physiological data—such as activity levels, sleep patterns, or voice tone—to detect warning signs of mental health deterioration.

Example: Torous et al. (2019) discussed how wearable technology combined with AI could predict depressive episodes, allowing for timely interventions.

A clinical scenario: A patient recovering from major depressive disorder uses a smartwatch that tracks sleep and activity. When deviations suggesting an oncoming depressive episode occur, the AI sends an alert to the patient and their clinician, prompting early intervention.

A tool to explore: mindLAMP is an open-source platform that integrates digital phenotyping with AI analytics, offering insights into mental health trends and supporting personalised treatment.

Ethical considerations in integrating artificial intelligence

The use of AI in mental health care raises ethical questions that demand careful consideration to avoid harm and maintain trust in therapeutic relationships.

Patient privacy and confidentiality

AI systems require large datasets, often containing sensitive patient information. Protecting this data is critical to maintaining patient trust. Clinicians must ensure compliance with privacy laws such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA).

Action steps:

  • Obtain explicit informed consent for data collection and usage.
  • Use encrypted, secure platforms to store and process patient information.
  • Regularly review data policies to ensure they align with legal and ethical standards.
  • Review your professional association’s and/or credentialing body’s ethics codes and guidelines to stay up-to-date with ethical standards for your profession.

Bias and fairness

AI systems can reflect and amplify biases present in their training data. This may result in unequal treatment recommendations for different demographic groups, potentially exacerbating disparities in mental health care.

Example: Brunn et al. (2020) reported that some AI diagnostic tools performed less effectively for minority groups due to insufficient representation in training datasets.

Action steps:

  • Choose AI tools developed with diverse datasets (as much as possible).
  • Advocate for transparency in AI model design to understand how decisions are made.
  • Regularly audit AI tools for biases to ensure fairness.

Clinical responsibility and oversight

AI tools are not infallible. Clinicians must retain ultimate responsibility for diagnosis and treatment decisions, ensuring AI serves as an adjunct rather than a replacement.

Action steps:

  • Use AI outputs as one of many factors in decision-making.
  • Regularly update knowledge about the capabilities and limitations of AI.
  • Engage patients in discussions about how AI is being used in their care to promote transparency and collaboration.

Conclusion

Artificial intelligence has immense potential to revolutionise mental health care by enhancing diagnosis, tailoring treatments, and providing continuous monitoring. However, its integration must be approached with caution, ensuring ethical principles guide its use. As a mental health professional, you are uniquely positioned to balance technological innovation with the human touch, maintaining empathy and trust as the cornerstones of clinical care.

Key takeaways

  • AI applications in mental health include diagnostic support, personalised treatment planning, chatbots, notetaking, and predictive analytics.
  • Ethical integration of AI requires attention to patient privacy, mitigation of biases, and maintaining clinical oversight.
  • Clinicians must prioritise transparency and inclusivity in adopting AI tools.
  • Tools like Woebot, mindLAMP, among many others, offer practical ways to incorporate AI into mental health care.

References

  • Brunn, M., Diefenbacher, A., Courtet, P., & Genieys, W. (2020). The future is knocking: How artificial intelligence will fundamentally change psychiatry. Academic Psychiatry, 44(4), 461–466.
  • Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., & Choi, K. (2021). The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry, 20(2), 154–170.
  • Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behaviour therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomised controlled trial. JMIR Mental Health, 4(2), e19.
  • Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018). Using psychological artificial intelligence (Tess) to relieve symptoms of depression and anxiety: Randomised controlled trial. JMIR Mental Health, 5(4), e64.
  • Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H. C., & Jeste, D. V. (2019). Artificial intelligence for mental health and mental illnesses: An overview. Current Psychiatry Reports, 21(11), 116.
  • Nebeker, C., Torous, J., & Bartlett Ellis, R. J. (2019). Building the case for actionable ethics in digital health research supported by artificial intelligence. BMC medicine, 17(1), 137. https://doi.org/10.1186/s12916-019-1377-7