Most patient-facing clinical workers will likely hold onto their jobs as AI advances, but roles heavy on documentation, administrative tasks, and diagnostic dot-connecting face significant risk. So predicts medical AI expert Jesse Pines, MD, MBA, of George Washington University, Drexel University, and US Acute Care Solutions, where he serves as chief of clinical innovation.
Writing in Forbes, Pines outlined which healthcare jobs could disappear, which will stay, and the new positions taking shape. He stressed that non-technological factors - regulatory frameworks, liability laws, and public trust in autonomous medical AI - will heavily influence the technology's job-replacement rate over the next decade. AI is unlikely to swipe jobs "demanding high-stakes human connection, fine motor skills and complex decision-making such as nursing or surgery," he wrote.
The six jobs at greatest risk
Pines identified six titles facing the most immediate threat from automation:
- Human scribe
- Medical coder
- Appointment scheduler
- Front-desk receptionist
- Insurance verification specialist
- Pharmacy technician
Roles AI is least likely to replace
Meanwhile, eight titles sit safely in the list of roles AI will leave intact. These include registered nurse, paramedic/EMT, mental health therapist, midwife, home health aide/certified nursing assistant, and dental hygienist. Pines also named three physician-level jobs as highly secure: surgeon, emergency medicine specialist, and dentist.
For healthcare workers who want to stay relevant, Pines advised they "cultivate AI literacy to understand its capabilities and limitations." He warned that the worst stance is "passive avoidance, such as waiting until the tools are mandated and then scrambling to adapt."
New jobs taking shape
The shifts in AI for Healthcare are creating new roles that blend clinical knowledge with technical skill. Pines described three emerging positions:
Clinical AI implementation specialist. These professionals serve as translators between technology teams and clinical stakeholders, overseeing deployment, adoption, and ongoing evaluation of AI tools. "The role typically requires a clinical background: nursing, pharmacy, respiratory therapy or allied health combined with training in health informatics and change management," Pines wrote. Indeed.com data puts the average salary at $70,000 to $100,000 per year.
Healthcare AI ethics and governance analyst. As AI systems take on consequential clinical and administrative roles, health systems need dedicated analysts to evaluate systems for bias, fairness, safety, and regulatory compliance. They review algorithm performance across patient subpopulations, maintain documentation for audits, design clinical validation frameworks, and advise leadership on risk. ZipRecruiter reports an average annual salary of $141,139.
Health AI data scientist/clinical data engineer. Health systems generate massive stores of clinical data, and they need experts to curate, label, and transform raw inputs into training datasets and validated AI models. These roles require proficiency in Python or R, SQL, machine learning frameworks such as TensorFlow or PyTorch, and a working understanding of clinical terminologies like SNOMED, LOINC, and HL7 FHIR. ZipRecruiter data shows an average annual salary of $122,738.
Why this matters for healthcare professionals
The safest move isn't to ignore AI but to engage with it early. Pines recommended a direct strategy: "Seek out opportunities now to interact with AI systems in your clinical area, whether that means participating in a pilot program, attending a CME course in clinical informatics or simply reading peer-reviewed literature on AI performance in your specialty." The roles most shielded from automation are those built on high-stakes human connection and complex manual skills-but anyone in the field can strengthen their position by understanding what AI can and cannot do.
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