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In brief
- Start with a clearly defined clinical or administrative problem, not with an AI product looking for a use.
- Ask for evidence from settings similar to yours, and test locally before relying on any tool.
- Keep a qualified person responsible for every clinical decision, and design review so it is genuinely easy.
- Protect patient data, check for bias, fit the workflow and keep monitoring after launch.
Artificial intelligence is arriving in healthcare from many directions at once: documentation assistants, symptom checkers, image analysis, chat tools for patients, and general-purpose language models that clinicians use informally. Some of these tools are genuinely helpful. Some are not ready. Many are somewhere in between, useful in one setting and risky in another.
Working in technology and training in clinical medicine, I am asked for a simple way to judge them. There is no single test, but there is a set of questions that sorts most tools quickly. This checklist is written for clinicians, practice owners and technologists, and it is deliberately practical.
Before anything else: what problem are you solving?
1. Is the problem specific?
"Use AI in our clinic" is not a problem. "Doctors spend forty minutes after each session writing notes" or "follow up reminders are often missed" are problems. A specific problem lets you judge whether a tool helps and how you would measure it.
2. Is AI the right kind of solution?
Many healthcare problems are better solved by a simpler change: a clearer process, a checklist, a better form or a basic reminder system. AI adds cost, complexity and new failure modes. It should be chosen because it does something simpler tools cannot, not because it is available.
Evidence and performance
3. What evidence supports the tool, and from where?
Ask what the tool was tested on and in what setting. A tool evaluated on data from large urban hospitals may perform differently in a small practice with different patients, languages and record-keeping habits. Published, independent evaluation is stronger than vendor claims.
4. Have you tested it on your own cases?
Before relying on a tool, run it in the background on real, appropriately protected cases and compare its outputs with what your clinicians would do. Record disagreements and understand them. This is slower than switching it on, and far safer.
5. How does it behave when it is uncertain or wrong?
Every tool makes mistakes. The important question is how. Does it signal uncertainty? Does it show its sources? Does it fail loudly, or does it produce a plausible answer that is wrong? A tool that says "I am not sure" is often more useful than one that never does.
A particular risk with language models
General-purpose language models can produce fluent, confident statements about medicines, doses or conditions that are incorrect. Any clinical information from such a tool must be verified against trusted sources before it influences care.
Human oversight and accountability
6. Who is responsible for decisions the tool influences?
The answer should be a named, qualified professional. AI can inform a decision; it cannot be accountable for one. If the design or contract of a tool leaves responsibility unclear, that is a serious concern.
7. Is review genuinely easy?
If checking an AI output takes as long as doing the task, busy clinicians will stop checking. Good tools show what they concluded, why, and what is unusual about the case, so that attention goes where it is needed. Easy override matters as much as easy approval.
8. Will clinical skills be preserved?
Over-reliance is a real risk, especially for students and early career clinicians. Tools should support reasoning rather than replace it. For training settings, consider how learners will develop independent judgement if suggestions are always available.
Privacy, fairness and workflow
9. How is patient data handled?
Ask where data is sent, stored and processed, who can access it, how long it is kept and whether it is used to train future models. Obtain informed consent where required, collect only what is necessary, and comply with applicable law, including India's Digital Personal Data Protection Act, 2023, as its rules take effect. Never paste identifiable patient information into consumer AI tools that are not approved for that use.
10. Could it perform worse for some patients?
Tools trained on data that under-represents certain groups (by age, sex, language, region or condition) may be less accurate for them. Ask whether performance has been checked across groups relevant to your patients.
11. Does it fit the way care actually happens?
A tool that requires extra logins, repeated data entry or a screen between clinician and patient will be resented and eventually bypassed. Observe the real workflow before adoption, and involve the people who will use the tool in choosing it. I have written more about this in The Consultation Is the Data.
After launch
12. How will you keep watching it?
Performance can change when patient populations, record formats or the tool itself change. Decide in advance what you will measure (errors caught, time saved, complaints, near misses), who will review it, and how the tool can be paused safely if problems appear.
A simple scoring approach
For a quick first assessment, rate each question as clear, partly clear or unclear.
| Result | Suggested action |
|---|---|
| Mostly clear | Proceed to a careful local trial with monitoring |
| Several partly clear | Resolve open questions with the vendor or team before any trial |
| Any unclear on questions 6, 9 or 10 | Do not use the tool with patients until resolved |
This is not a formal regulatory assessment, and regulated medical devices and software have their own requirements. It is a practical way to make better early decisions.
The principle underneath the checklist
Every question above comes back to one idea, which runs through all my work on technology and healthcare convergence: technology should support healthcare professionals and never replace human judgement, individualisation or empathy. A tool that makes clinicians faster but less attentive has failed, however impressive its accuracy figures.
Questions
Frequently asked questions
Is it safe for doctors to use general AI chat tools for clinical questions?
General-purpose AI tools can help with tasks such as drafting patient education material or summarising non-identifiable information, but they can produce incorrect medical statements. Any clinical information must be verified against trusted sources, identifiable patient data should not be entered into unapproved tools, and clinical decisions remain the clinician's responsibility.
What is the most important question when adopting AI in healthcare?
Who is accountable for decisions the tool influences. A qualified professional must remain responsible, and the tool must be designed so that meaningful human review is practical in everyday work.
Does AI in healthcare need to be regulated?
Many AI tools that diagnose, treat or guide clinical decisions fall under medical device or software regulations in various countries, and data protection laws apply to patient data. Requirements differ by jurisdiction and change over time, so organisations should check the current rules that apply to them.
