Over the past year, many clinicians have noticed a new kind of exhaustion creeping into their work. It is not the familiar emotional fatigue that comes from holding space for others, nor is it purely administrative burnout. It is something more subtle. A constant stream of new AI tools, updates, prompts, platforms, and promises, all claiming to make practice easier, faster, and smarter. Instead of relief, many clinicians feel overwhelmed, distracted, and unsure where to focus.
This is what AI fatigue looks like in clinical practice.
At its core, AI fatigue is not about technology itself. It is about cognitive overload. Clinicians are already managing complex caseloads, ethical responsibilities, documentation demands, and emotional labour. When AI enters the picture without clear boundaries or purpose, it can add noise rather than clarity. The result is not better care, but fragmented attention and reduced clinical presence.
One of the main reasons AI fatigue develops is the assumption that more tools automatically mean better outcomes. In reality, clinical work does not benefit from constant switching. Each new platform requires learning, evaluation, and mental energy. When clinicians try to keep up with every new release, they often spend more time managing tools than thinking clinically. This erodes one of the most valuable resources in therapy. Deep, uninterrupted reasoning.
Another contributor is the pressure to use AI simply because it exists. There is an unspoken fear of falling behind or not being innovative enough. But clinical excellence has never been about using the most tools. It has always been about using the right ones, deliberately and ethically. Innovation without intention rarely improves practice.
It is also important to recognise that not all AI tools are designed with clinicians in mind. Many are built for speed, content generation, or surface-level productivity. Therapy, assessment, and diagnosis require something different. They require nuance, uncertainty, and tolerance for complexity. Tools that promise instant answers can subtly undermine reflective thinking, especially when clinicians are already tired.
Choosing what to ignore is therefore not a failure. It is a clinical skill.
A helpful starting point is to ask a simple question before adopting any AI tool. What cognitive load is this actually reducing? If a tool saves time on administrative tasks like drafting reports, summarising notes, or organising information, it may protect mental energy for clinical reasoning. If it adds another system to check, another output to evaluate, or another workflow to manage, it may be costing more than it gives.
Another key filter is alignment with clinical values. Tools should support evidence-based thinking, not shortcut it. They should help clinicians think more clearly, not think less. If a tool encourages copying, over-reliance, or uncritical acceptance of outputs, it deserves skepticism. Good AI use feels supportive, not directive.
It is also worth limiting the number of tools used at any one time. In practice, most clinicians only need one or two AI supports that fit naturally into their workflow. For example, one tool for structured thinking or documentation support. One tool for communication or explanation. Anything beyond that should earn its place clearly.
AI fatigue also decreases when clinicians shift from tool hunting to purpose clarity. Instead of asking what new AI tool is available, ask where the friction points are in your own practice. Is it report writing? Parent communication? Case conceptualisation? Admin backlog? Start with the problem, not the platform. This alone filters out most unnecessary noise.
Crucially, AI should never replace reflective pauses. Some of the most important clinical insights come from sitting with uncertainty, reviewing patterns over time, or discussing cases with colleagues. If AI use crowds out these processes, it is being misused. Technology should create space for thinking, not fill every gap.
There is also a cultural aspect to address. Clinicians need permission to disengage from constant updates. Not every release is relevant. Not every feature needs testing. Staying informed does not mean staying flooded. Sustainable practice requires boundaries, including digital ones.
Ultimately, the goal is not to become an AI-powered clinician. It is to remain a thoughtful, present, evidence-based one in a rapidly changing environment. AI can be a valuable support when used intentionally. It can reduce friction, organise complexity, and protect time. But only when clinicians remain in control of when, why, and how it is used.
In a field built on human connection and clinical judgment, the most responsible use of AI may sometimes be choosing not to use it at all.