AI in Healthcare: What Every Medical Professional Should Know

Here’s something that doesn’t get talked about enough: healthcare is facing a 10 million worker shortage by 2030, according to the World Health Organization. At the same time, it’s undergoing the most significant AI transformation of any industry. Those two facts aren’t in conflict — they’re deeply connected, and understanding how they interact is the key to navigating your healthcare career over the next five years.

We covered this extensively on the podcast, and it’s one of the episodes that generated the most listener questions we’ve ever had:

“I have family members in healthcare — nurses, specifically — and I see the anxiety firsthand,” says James Perkins, co-founder of What About AI? and an enterprise technology leader with 18+ years in payments and fintech. “But when I talk to them about what’s actually happening, the fear is almost always about the wrong thing. They’re worried about robots replacing bedside care. The real change is administrative AI handling the documentation burden that’s been crushing them for years.”

This guide is structured around your specific role. If you’re looking for the latest AI healthcare news and policy developments, we have a companion piece: AI & Healthcare: What’s Happening Right Now. This post is the career guide — what the data says about your profession, and what you should do about it.

Where AI Is Already Deployed in Healthcare

This isn’t a speculative future. These are systems in active clinical use right now:

• Diagnostic imaging: AI-assisted radiology reduced diagnostic errors by 34% in a multi-site trial published in JAMA in early 2025. Systems like Viz.ai are flagging strokes in CT scans and routing patients to specialists faster than traditional workflows.
• Clinical documentation: Ambient listening tools (like Nuance DAX Copilot, now integrated into Epic) transcribe patient encounters in real time, generating clinical notes that physicians review and sign. The AMA reports that 65% of physicians now use some form of AI-assisted clinical tool.
• Drug discovery: AI models are compressing early-stage drug screening from years to months. Insilico Medicine’s AI-discovered drug candidate reached Phase II trials — a timeline that would have been unthinkable five years ago.
• Billing and coding: UnitedHealth Group and Anthem have both announced AI auto-coding pilot programs that process claims without human review for routine cases.
• Administrative triage: AI chatbots handle appointment scheduling, symptom pre-screening, and insurance verification at major health systems including Kaiser Permanente and Cleveland Clinic.

A study in Nature Medicine found that AI diagnostic systems matched or exceeded specialist performance in 12 of 14 clinical areas studied. And Accenture estimates that AI applications in healthcare could save $150 billion annually by 2026. The technology isn’t arriving — it’s here.

The Role-by-Role Reality

We track displacement risk scores for every major healthcare profession in our database. Here’s an honest breakdown based on current data:

Low Risk (Under 25%)

Registered Nurse (~17% displacement risk) — The Bureau of Labor Statistics projects RN employment to grow 6% through 2032, and that’s before accounting for the global shortage. Nursing requires physical presence, emotional intelligence, complex clinical judgment, and patient trust. AI will transform what nurses spend their time on, but it won’t replace the role.

Physical Therapist (~15%) — Hands-on treatment, real-time adaptation to patient movement, and the motivational relationship between therapist and patient make this one of the most AI-resistant roles in all of healthcare.

Therapist/Counselor (~18%) — Mental health care is fundamentally relational. AI therapy chatbots exist, but clinical outcomes research consistently shows that the therapeutic alliance — the human relationship — is the single strongest predictor of treatment success.

Surgeon (~20%) — Robotic surgery is expanding, but these are surgeon-controlled tools, not autonomous systems. The liability, regulatory, and trust barriers to autonomous surgical AI are enormous and will remain so for the foreseeable future.

Physician/Doctor (~22%) — Physicians face significant workflow changes but low displacement risk. AI assists with diagnosis and documentation; physicians retain clinical decision-making authority, patient relationships, and the legal accountability that the system is built around.

Medium Risk (25–65%)

Radiologist (~52%) — This has been the headline role in AI healthcare discussions for years. The reality is more nuanced than “AI will replace radiologists.” AI reads routine scans faster and more consistently, but complex cases, interventional radiology, and clinical consultation remain deeply human. The role is shifting toward oversight and interpretation of AI-flagged findings.

Pharmacist (~55%) — Automated dispensing, AI drug interaction checking, and mail-order pharmacy are compressing the traditional pharmacist role. The growth area is clinical pharmacy — medication therapy management, patient counseling, and integration into care teams.

High Risk (65%+)

Medical Lab Technologist (~65%) — Automated analyzers and AI-powered image recognition for pathology slides are reducing the need for manual lab work. Technologists who specialize in complex or novel testing, quality assurance, and equipment management will have more staying power.

Health Information Technician (~72%) — Data entry, records management, and information coding are core AI automation targets. This role is being absorbed into broader health informatics positions that require analytical skills.

Medical Coder/Biller (~79%) — This is the starkest number in our healthcare data. “Medical coding is the one area where the data is genuinely difficult to sugarcoat,” says Sean Boyce, co-founder of What About AI? and a tech executive who consults with healthcare organizations on AI implementation. “When two major insurers announced AI auto-coding pilots, the displacement risk in our model jumped from 74% to 79% in a single week. That’s not a gradual shift — it’s an industry signal.”

The Patient Factor

There’s a powerful protection that data-only analyses miss: patients prefer human care. Survey after survey shows that while patients are comfortable with AI handling scheduling, billing, and even preliminary diagnostics, they overwhelmingly want a human being making treatment decisions, delivering diagnoses, and providing emotional support.

This isn’t irrational. Healthcare is experienced during some of the most vulnerable moments of a person’s life. The empathy, reassurance, and human connection that healthcare professionals provide isn’t a nice-to-have — it’s a core part of the therapeutic process. Roles that are closest to the patient have a structural protection that pure efficiency arguments can’t override.

That said, patient preferences can shift. If AI diagnostics demonstrate consistently better outcomes and faster access (especially in underserved areas facing severe provider shortages), public trust will follow. The protection is real but not permanent — which is why adaptation matters regardless of your current risk score.

What Healthcare Professionals Should Do Right Now

“Every healthcare system I’ve talked to is desperate for AI to handle documentation and billing,” says Sean. “Nurses spend 25 to 30 percent of their time on paperwork that AI can handle. McKinsey pegs administrative tasks at 25 to 30 percent of all healthcare worker time. So here’s the counterintuitive thing: AI could actually give nurses and physicians more time for patient care, not less. The professionals who understand that framing will lead this transition instead of being threatened by it.”

Here’s what we recommend based on your role:

If You’re in a Low-Risk Role (Nurse, Physician, Therapist, Surgeon, PT)

• Learn the AI tools entering your workflow. Ambient documentation (DAX Copilot, Abridge), clinical decision support systems, and AI-powered patient monitoring are coming to your workplace if they haven’t already. Be the person on your team who knows how to use them well.
• Double down on what AI can’t do. Complex clinical reasoning, patient communication, interdisciplinary coordination, and leadership. These skills become more valuable as AI handles the routine.
• Advocate for good implementation. AI tools deployed badly create more work, not less. Clinicians who can articulate what works and what doesn’t are invaluable to their organizations right now.

If You’re in a Medium-Risk Role (Radiologist, Pharmacist)

• Shift toward the human-judgment end of your specialty. For radiologists: interventional procedures, complex case consultation, AI system oversight. For pharmacists: clinical pharmacy, medication therapy management, patient education.
• Get certified in AI-adjacent skills. Health informatics, data analytics, and AI system validation are growing fields that leverage your clinical expertise.
• Consider hybrid roles. Clinical informaticists, AI implementation specialists, and digital health strategists are positions that didn’t exist five years ago and now pay well above traditional clinical salaries.

If You’re in a High-Risk Role (Medical Coder, Health Info Tech, Lab Tech)

• Start transitioning now, not later. The timeline for significant disruption in these roles is measured in years, not decades. Waiting for it to happen before responding puts you behind.
• Target adjacent roles that leverage your domain knowledge. Medical coders understand healthcare revenue cycles deeply — revenue cycle management, compliance auditing, and AI system training all need that expertise. Lab technologists can move toward quality assurance, specialized testing, or laboratory informatics.
• Build technical skills. SQL, basic data analysis, health informatics certifications (AHIMA, AMIA) — these bridge your healthcare knowledge into roles with better long-term prospects.
• Take our career risk quiz for a personalized assessment: whataboutai.com/quiz

The Bottom Line

Healthcare isn’t going to have fewer jobs because of AI — the worker shortage alone prevents that. But it is going to have different jobs. The roles that center on human connection, complex judgment, and physical presence are not only safe but likely to become more rewarding as AI strips away the administrative burden. The roles that center on data processing, pattern matching, and routine documentation are being automated rapidly.

The professionals who thrive will be the ones who understand which category their work falls into — and act accordingly.

Sources

Find Your Healthcare Role’s Risk Score

Our database covers every major healthcare profession with specific displacement scores, timeline estimates, and adaptation strategies.

• Healthcare industry breakdown → whataboutai.com/jobs/healthcare
• AI & Healthcare deep dive → whataboutai.com/blog/ai-healthcare
• Take the career risk quiz → whataboutai.com/quiz

Coaching: For personalized career guidance in healthcare, visit whataboutai.com/coaching.