Wael Salloum
Technology Review
Originally posted 28 Aug 25
Over the past 20 years building advanced AI systems—from academic labs to enterprise deployments—I’ve witnessed AI’s waves of success rise and fall. My journey began during the “AI Winter,” when billions were invested in expert systems that ultimately underdelivered. Flash forward to today: large language models (LLMs) represent a quantum leap forward, but their prompt-based adoption is similarly overhyped, as it’s essentially a rule-based approach disguised in natural language.
At Ensemble, the leading revenue cycle management (RCM) company for hospitals, we focus on overcoming model limitations by investing in what we believe is the next step in AI evolution: grounding LLMs in facts and logic through neuro-symbolic AI. Our in-house AI incubator pairs elite AI researchers with health-care experts to develop agentic systems powered by a neuro-symbolic AI framework. This bridges LLMs’ intuitive power with the precision of symbolic representation and reasoning.
Here are some thoughts:
This article is of interest to psychologists because it highlights the real-world integration of agentic AI—intelligent systems that act autonomously—within complex healthcare environments, a domain increasingly relevant to mental and behavioral health. While focused on revenue cycle management, the article describes AI systems that interpret clinical data, generate evidence-based appeals, and engage patients through natural language, all using a neuro-symbolic framework that combines large language models with structured logic to reduce errors and ensure compliance. As AI expands into clinical settings, psychologists must engage with these systems to ensure they enhance, rather than disrupt, therapeutic relationships, ethical standards, and provider well-being.
