What happens to clinical skills when AI does the routine work?

“The need to be extra vigilant when you're reviewing something versus doing it from scratch can be a real problem.”

By
Doug Brunk
September 1, 2026
7 min

MDSpire News

Objective:

To explore the impact of AI on clinical care and the daily work of clinicians.

Approach:

Panel Discussion: Experts discussed the clinical value of AI, workforce preparation, and lessons learned from AI implementation.

Key Findings:

AI can relieve administrative burdens and enhance clinician-patient relationships, as discussed by Erin Losey.
AI has potential applications in medication safety and clinical decision-making, as highlighted by Scott Nelson.
AI tools can automate tasks in radiation oncology, but there are concerns about de-skilling among trainees, as noted by Benjamin Kann.
Clinicians need a fundamental understanding of AI models and hands-on experience to effectively use AI tools, as emphasized by Travis Zack.
Feedback mechanisms are essential for improving AI systems and avoiding unintended consequences, as discussed by the panelists.

Interpretation:

The integration of AI into clinical practice presents opportunities for efficiency while raising challenges related to clinician skills and training, as highlighted by the panelists.

Limitations:

Concerns about de-skilling as AI takes over routine tasks were raised by panelists.
There is potential for AI to shift workload rather than reduce it, as discussed during the panel.
Panelists noted the risk of over-reliance on AI results.