Using AI to Diagnose and Treat Depression

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Table of Contents

Introduction
Discussion
Conclusion
References

Introduction

Clinical treatment of depression can be seriously optimized using AI technologies. One of the main features of AI is the ability to machine learning, that is, to use data from past experiences to learn and modify algorithms in the future (Greener et al., 2022). This is similar to more fine-tuning information processing mechanisms, including prediction methods.

Discussion

Applied to medicine, machine learning is proving to be a tool for continually improving inference, which is critical to the challenges of modern clinical science. For example, by offering AI big data on what signs have been reported in people with psychological depressive illnesses, such as loss of appetite, weight loss, and suicidal ideation, it will eventually be possible to create an effective, fact-based mechanism for diagnosing depression (Rajawat et al., 2021). Psychotherapists would be able to use such a tool for preventive, optimized, and unbiased diagnosis, which in turn would allow for more rapid treatment decisions.

From a broader perspective, AI with machine learning technologies could be incorporated into societys daily routine so as to automatically respond to users behavior, anonymously study their posts and publications, and track online activity and search queries. Based on such big data, this AI would be able to proactively diagnose any disruptive changes in a patient and notify them before they are assigned a psychotherapist, which would generally increase the availability and popularity of psychotherapeutic self-care. Indeed, this solution comes with many ethical risks and privacy concerns, but with proper management, it could prove to be an excellent tool (Guan, 2019). In addition, human enhancement can be implemented with AI in the treatment phases as well.

Conclusion

AI chatbots can maintain constant communication and become a valuable tool for electronic medical records, providing contact between the patient and the clinical care provider (Meng & Dai, 2021). For example, an individual with already diagnosed depression could use such chatbots to describe their moods and feelings, record observations and explore dynamics, and in turn, receive advice and recommendations from the treating physician directly.

References

Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022). A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology, 23(1), 40-55. Web.

Guan, J. (2019). Artificial intelligence in healthcare and medicine: Promises, ethical challenges and governance. Chinese Medical Sciences Journal, 34(2), 76-83. Web.

Meng, J., & Dai, Y. (2021). Emotional support from AI chatbots: Should a supportive partner self-disclose or not?Journal of Computer-Mediated Communication, 26(4), 207-222. Web.

Rajawat, A. S., Rawat, R., Barhanpurkar, K., Shaw, R. N., & Ghosh, A. (2021). Depression detection for elderly people using AI robotic systems leveraging the NelderMead Method [PDF document]. Web.

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Using AI to Diagnose and Treat Depression

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