7 Questions to Answer Before Implementing Any Clinical AI

I’ve developed a personal checklist over the years—questions I ask before recommending any clinical AI implementation. Some come from success. More come from failures I’ve witnessed or been part of.

These aren’t exhaustive, but they’re the questions that matter most.

Question 1: What Problem Are We Actually Solving?

This sounds obvious. It’s often not answered clearly.

“We want to use AI in radiology” isn’t a problem statement. “We want to reduce time-to-diagnosis for urgent chest findings” is.

A clear problem statement should include:

  • What’s the current situation?
  • What’s wrong with it?
  • Who is affected?
  • What does “better” look like?
  • How would we measure improvement?

If you can’t articulate the problem precisely, you’re not ready to implement a solution.

Red flags:

  • “We need to be innovative”
  • “Our competitors are doing it”
  • “The board asked about AI”

These might be drivers for exploring AI, but they’re not problems AI solves.

Question 2: Does This AI Actually Work?

Vendor demonstrations are convincing. Marketing materials are polished. That doesn’t mean the AI works.

To assess effectiveness:

Demand evidence, not demonstrations. Published, peer-reviewed validation studies. Not vendor-produced white papers—independent evaluation.

Assess evidence relevance. Was the AI validated on patients similar to yours? In clinical environments similar to yours? “Works well at a US academic medical centre” doesn’t guarantee it works in your Australian regional hospital.

Understand performance metrics. Sensitivity, specificity, positive predictive value—what do they actually mean for your clinical context? A 95% sensitivity sounds great until you realise the 5% miss rate affects real patients.

Ask about failure modes. Where does this AI perform poorly? Every system has limitations. Vendors who claim universal excellence aren’t being honest.

Check the training data. What data was the AI trained on? How old is it? How representative?

Red flags:

  • Vendor won’t share detailed performance data
  • No independent validation studies
  • Evidence only from very different populations or settings
  • Vague claims without specific metrics

Question 3: Do We Have the Capability to Implement This Successfully?

Implementation capability includes:

Technical capability. Can your IT team integrate this AI with your clinical systems? Do you have necessary infrastructure? Do you understand what’s actually required?

Clinical informatics capability. Do you have people who understand both clinical workflows and AI systems? Who will bridge between clinicians and technology?

Change management capability. Do you have experience with clinical technology change? Training resources? Change management expertise?

Governance capability. Can you monitor AI performance over time? Handle incidents? Make ongoing decisions about AI use?

If these capabilities don’t exist, you need to build them or find partners who can provide them. AI consultants Sydney and similar firms can supplement internal capability, but you still need some internal foundation.

Red flags:

  • Expecting vendor to handle everything
  • No internal AI expertise
  • History of failed technology implementations
  • Clinical staff not engaged in planning

Question 4: What Could Go Wrong, and Are We Prepared?

Every AI deployment has risks. Mature organisations identify and plan for them.

Clinical risks:

  • AI makes errors that affect patient care
  • Clinicians over-rely on AI and miss things AI misses
  • AI unavailability during critical moments
  • Performance degradation over time

Technical risks:

  • Integration failures
  • Performance under real-world conditions
  • Security vulnerabilities
  • Vendor stability

Organisational risks:

  • Clinical staff resistance
  • Implementation delays and cost overruns
  • Regulatory compliance issues
  • Reputational damage from incidents

For each significant risk, you need mitigation plans. What will you do when (not if) problems occur?

Red flags:

  • No documented risk assessment
  • Assumption that everything will work as planned
  • No contingency for AI unavailability
  • No incident response planning

Question 5: What Will This Really Cost?

Healthcare AI costs more than vendors suggest. A realistic cost assessment includes:

Acquisition costs:

  • Licensing fees (initial and ongoing)
  • Implementation services
  • Hardware or cloud infrastructure

Integration costs:

  • Technical integration development
  • Testing and validation
  • Data migration or preparation

Change management costs:

  • Training development and delivery
  • Workflow redesign
  • Clinical time during transition
  • Productivity dip during learning period

Ongoing costs:

  • Support and maintenance
  • Performance monitoring
  • Governance overhead
  • Periodic retraining or updates

Hidden costs:

  • IT team time diverted from other priorities
  • Clinical informatics time
  • Executive time and attention
  • Opportunity cost of not doing something else

Model these costs over five years. Compare to expected benefits honestly. Don’t assume optimistic scenarios.

Red flags:

  • Cost estimates only include licensing
  • No budget for integration or change management
  • Benefits assumptions that seem unrealistic
  • No ongoing cost projection

Question 6: How Will We Know If It’s Working?

Success metrics should be defined before implementation:

Clinical metrics:

  • What clinical outcomes should improve?
  • What’s the baseline?
  • What improvement is clinically meaningful?
  • When would we expect to see results?

Operational metrics:

  • Efficiency gains (time, throughput)
  • Quality improvements (accuracy, consistency)
  • User satisfaction

Financial metrics:

  • Return on investment
  • Cost avoidance
  • Revenue impact (if applicable)

Define these metrics, establish baselines, and plan measurement. If you can’t measure success, how will you know if you’ve achieved it?

Red flags:

  • “We’ll figure out measurement later”
  • Success defined vaguely (“improved care”)
  • No baseline data
  • Measurement requires infrastructure that doesn’t exist

Question 7: What’s Our Exit Strategy?

Sometimes AI implementations fail. Sometimes they succeed but circumstances change. Sometimes better alternatives emerge.

You need to be able to stop or change AI use:

Contractual flexibility:

  • What are the exit provisions in your vendor contract?
  • What’s the notice period?
  • What happens to your data when you leave?

Operational continuity:

  • Can clinical operations continue without the AI?
  • Have you maintained capability to work without it?
  • Is there a transition plan if you discontinue?

Data portability:

  • Can you export data from the AI system?
  • In what format?
  • Who owns derived data (AI outputs, performance data)?

Decision triggers:

  • Under what circumstances would you discontinue?
  • Who makes that decision?
  • How quickly could discontinuation happen?

Red flags:

  • Long-term contracts with no exit
  • Complete dependency on AI with no fallback
  • No clarity on data ownership
  • No defined decision process for discontinuation

Using These Questions

Before any clinical AI implementation, work through these questions. Write down your answers. Share them with stakeholders. If you can’t answer them clearly, you’re not ready.

These questions don’t guarantee success. But they reveal gaps that, if addressed, improve your chances significantly.

And sometimes, the right answer is “not yet” or “not this AI.” Knowing when to wait is as important as knowing when to proceed.

Dr. Rebecca Liu is a health informatics specialist and former Chief Clinical Information Officer. She advises healthcare organisations on clinical AI strategy and implementation.