Interesting article, I did not write, about explaining what is now being encountered as Psychosis and LLM Sycophancy, but I also have some questions regarding this article.

Understanding Emerging Concerns in AI: Psychosis and Sycophancy in Large Language Models

In recent discussions surrounding artificial intelligence, particularly large language models (LLMs), there’s emerging dialogue about phenomena that resemble human psychological conditions, notably “psychosis” and “sycophancy.” An intriguing article examines these parallels, exploring how the behaviors of AI systems might mirror certain mental health symptoms. You can read the full piece here: [Link to the article].

A central question arises: can we quantify and track these phenomena? Specifically, if we observe the frequency with which AI-generated content influences an individual’s perceptions or behaviors, could we develop straightforward assessment tools—perhaps during clinical intake—to gauge the impact? Such tools might include simple questionnaires that measure one’s exposure to AI influences, helping researchers and clinicians understand the prevalence of what might be described as “AI-induced psychosis.”

From a healthcare perspective, recognizing these behaviors could prompt further investigation into their causes and whether they qualify as diagnosable conditions within Western medical frameworks. However, establishing such diagnoses would require concrete evidence and, crucially, effective treatments. Without validated interventions, labeling these as mental health disorders remains speculative.

My understanding of medicine is shaped by foundational texts like Kaplan and Sadock’s Psychiatry and Michel Foucault’s The Birth of the Clinic. While these works provide valuable insights, they also highlight certain systemic critiques—particularly how economic interests often influence medical practices and research priorities. Historically, shifts from monarchial to republican governance, along with the democratization of medical knowledge through scientific advancement, sought to ensure wider access to healthcare.

Today, in regions like Texas, there appears to be a tension between scientific progress and political change, influencing medical policy and practice. This underscores the importance of robust, data-driven approaches to understanding health trends—methods that could be greatly enhanced by artificial intelligence.

Indeed, AI could serve as a powerful tool to analyze public policies’ influence on medical literature or to assess the prevalence of phenomena such as AI-induced alterations in mental states. Yet, skepticism remains: can AI-based assessments be truly objective? The underlying algorithms—particularly those involving Reinforcement Learning with Human Feedback (RLHF)—may generate hallucinations or distortions of reality, challenging the notion of complete objectivity.

In summary, as AI systems become more integrated into our lives, it’s vital to approach these developments critically—balancing technological capabilities with awareness of their limitations. Whether examining AI’s psychological effects or analyzing policy shifts, leveraging AI thoughtfully