When I initially started engaging with clinical AI tools, I experienced the kind of thrill that many young clinicians and researchers currently experience. For the first time, it appeared feasible to alleviate cognitive strain, reveal hidden patterns, and allow clinicians to devote more time to what truly mattered: patients. AI seemed less like a menace and more like a long-anticipated ally.

As a budding clinician-scholar, I followed the path many of us take. I explored extensively, experimented with tools, and began articulating the implications of AI-enhanced decision-making. I was not opposing AI. Rather, I was advocating for something more specific and, I believed, more pressing: how we maintain clinical judgment in an age where machines are becoming increasingly capable, quick, and convincing.

The reality of academic pushback

Then reality stepped in.

As my work underwent formal academic scrutiny, I faced resistance that caught me off guard. Not animosity, but skepticism. I was consistently asked to “demonstrate” that the decline of diagnostic reasoning was already taking place, to justify why this issue warranted attention now instead of later. Some reviewers doubted whether such risks were even credible. Others implied that if AI improved outcomes, concerns about judgment were of lesser importance.

What disturbed me was not the rejection itself. Rejection is a facet of academic existence. What troubled me was the acknowledgment that the issue I was outlining lacked a recognized name, framework, or established context. Without extensive data or institutional backing, sounding early alarms felt less like scholarship and more like conjecture (at least in the perspective of the system).

For a period, this was profoundly disheartening. It seemed that enthusiasm for AI had left scant room for thoughtful introspection, particularly when that introspection originated from someone at the outset of their career. I started to question whether I had entirely misjudged my position. Was I too premature? Too hesitant? Or merely in the incorrect environment?

The risk of unexamined collaboration

Eventually, I understood that the matter was not whether AI should be utilized. That inquiry has already been resolved. The crucial question is how humans and AI learn to collaborate without undermining what gives clinical expertise its significance in the first place.

Clinical judgment is not a fixed ability. It is influenced by uncertainty, mistakes, reflection, and accountability. AI systems, conversely, provide clarity without responsibility. When their outputs are regarded as authoritative instead of advisory, the danger lies not in clinicians becoming obsolete, but in their detachment from the very reasoning processes that once characterized their proficiency.

This does not render AI hazardous. It makes unexamined collaboration hazardous.

Reframing the role of the clinician-scholar

What rejuvenated my sense of purpose was reinterpreting my role, not as an adversary of AI, nor as its proponent, but as a mediator between systems. Young clinicians and scholars occupy a distinctive position. We are sufficiently conversant with technology to perceive its potential, yet close enough to clinical training to acknowledge what may be quietly forfeited along the journey.

Hope, I have discovered, does not stem from blind optimism. It arises from thoughtful collaboration. AI can aid clinicians without supplanting judgment, but only if we intentionally devise training, workflows, and professional practices that keep humans cognitively engaged rather than submissive.

For others experiencing similar frustrations, particularly early in their careers, I extend this reassurance: Facing resistance does not imply your concern is unwarranted. It may simply indicate that you are positioned at the brink of a dialogue that has not yet entirely commenced.

AI will persist in evolving. The more challenging task (ensuring that human judgment evolves alongside it) belongs to all of us. And that task remains valuable.

Gerald Kuo, a doctoral candidate in the Graduate Institute of Business Administration at Fu Jen Catholic University in Taiwan, specializes in health care management, long-term care systems, AI governance in clinical and social care contexts, and elder care policy. He is associated with the Home Health Care Charity Association and maintains a professional presence on Facebook, where he shares insights on research and community initiatives. Kuo assists in operating a day-care center for seniors, collaborating closely with families, nurses, and community physicians. His research and practical endeavors focus on alleviating administrative pressure on clinicians, enhancing continuity and quality of elder care, and developing sustainable service models through data, technology, and interdisciplinary collaboration. He is particularly interested in how emerging AI tools can support aging clinical workforces, improve care delivery, and foster greater trust between health systems and the public.