Rethinking the loop in Health AI systems. Is Your AI Collaborating or Just Automating?
20 February 2026
For years, the "Human-in-the-Loop" (HIL) has been the safety blanket of the digital health industry—a comforting slogan used to reassure regulators and clinicians that a person is still "in charge." However, as we embed AI into complex sociotechnical healthcare organisations, it is becoming clear that HIL is often a misnomer that masks a lack of true agency.
Moving from Automation to Collaboration
In a traditional Human-in-the-Loop model, the system is designed around automation. The AI drives the inference process, and the human expert is relegated to the role of a safety net or a mere labeller who validates the machine’s output. This often leads to automation bias, where clinicians—under immense institutional pressure, reflexively accept algorithmic recommendations.
To build truly resilient health systems, we should pivot toward AI in the Loop (AI2L). As argued by Natarajan et al. (link to the full article below), AI2L is a collaboration-centric approach where the human expert remains the primary decision-maker, and the AI serves as a co-adaptive assistant that provides supplementary insights and perception.
The Sociotechnical Reality: Remote Monitoring as a Case Study
Nowhere is this distinction more critical than in Remote Patient Monitoring (RPM). Consider a continuous glucose monitor (CGM):
The HIL View: An autonomous agent adjusts insulin levels, seeking human intervention only for calibration.
The AI2L View: The device provides a rich data stream that assists a clinician and patient in co-developing a care plan.
In this broader context, the loop isn't just about a single data point; it’s about a Learning Loop that oversees model drift and equity, and a Governance Loop that manages the ethical trade-offs of deployment. When we focus on AI2L, we prioritise the Clinical Loop, ensuring that point-of-care decisions are informed by the patient’s contextual daily life, data that AI alone often misses.
If we evaluate systems solely on AI-centric metrics like precision and recall, HIL systems will always appear superior. But healthcare isn't a static benchmark. AI2L requires a holistic approach to evaluation that measures:
Human-AI Interaction Quality: Is the clinician empowered or overwhelmed?
Sociotechnical Alignment: Does the system adapt to local clinical workflows and cultural nuances?
Fairness and Trust: Are the data sources credible and the outcomes equitable?
Automation is most effective in well-defined tasks, such as identifying drug-drug interactions. But in the undefinable context of general medical diagnosis, human wisdom is irreplaceable. We need to stop asking if the human is "in" the loop and start ensuring the AI is properly integrated into the expert's workflow.