Understanding AI in Medicine: A Quick Guide
1. What is this site?
Mednosis curates the latest research on artificial intelligence in medicine. Every week, we review 50+ sources, score articles using our S.I.G.M.A. framework, and publish the top 10 developments.
Who is this for?
- Healthcare professionals: Get clinical context without hype
- Patients & public: Understand what's real vs. what's years away
- Researchers: Track what's actually validated
2. How to Read Medical AI Research Without Overreacting
The Research Hierarchy
Exploratory (Blue badge)
- • Early-stage research, hypothesis-generating
- • Often small sample sizes (n < 100)
- What this means: Interesting idea, but 5-10 years from clinical use
- Don't: Change your treatment based on this
Promising (Yellow badge)
- • Validated in controlled settings
- • Larger studies (n = 100-1,000s)
- • Needs real-world testing
- What this means: Could be available in 2-5 years
- Don't: Ask your doctor to use this yet
Practice-Changing (Orange badge)
- • Strong multi-center evidence
- • Published in top journals (Nature, NEJM, Lancet)
- • FDA reviewing or approved in other countries
- What this means: Likely to be standard practice in 1-3 years
- Do: Ask your doctor if they're aware of this research
Guideline-Level (Green badge)
- • FDA approved or in clinical guidelines
- • Already deployed at major hospitals
- • Reimbursed by insurance
- What this means: Available now at many hospitals
- Do: Ask your care team if this applies to you
3. What "Early Study" vs "Practice-Changing" Really Means
Sample Size Matters
| Study Size | Interpretation |
|---|---|
| n < 100 | Pilot study, very early |
| n = 100-500 | Small trial, needs validation |
| n = 500-5,000 | Medium trial, gaining confidence |
| n > 5,000 | Large trial, more reliable |
| Meta-analysis (10+ studies) | Strongest evidence |
Study Type Matters
Ranked by strength of evidence:
- Meta-analysis - Combines multiple studies
- Randomized Controlled Trial (RCT) - Gold standard
- Prospective cohort study - Follows patients forward
- Retrospective study - Looks back at existing data
- Case report - Single patient or small series
Validation Matters
Single hospital study
Could be overfitted to their patient population. Needs testing elsewhere.
Multi-center validation
Tested at 3+ different hospitals. More likely to generalize.
External validation
Trained at Hospital A, tested at Hospital B. Much stronger evidence.
4. Red Flags in Hype-y AI Articles
"AI outperforms doctors"
Often cherry-picked comparison. Doesn't account for AI + doctor collaboration. Look for: "AI assists doctors" instead
"Could save millions of lives"
Theoretical extrapolation. Assumes 100% adoption and perfect performance. Look for: "In this study, reduced mortality by X%"
"Revolutionary breakthrough"
Often incremental improvement. Look for: Actual performance numbers (sensitivity, specificity, AUC)
Press release with no peer-reviewed paper
Company announcements vs. published research. Look for: Published in Nature, NEJM, JAMA, Lancet
"AI diagnoses with 99% accuracy"
What's the baseline? (Human doctors, existing tests) What's the dataset? (Curated images vs. real-world messy data) Look for: Comparison to clinical standard of care
5. Questions to Ask
For Clinicians
- What's the sample size and study design?
- Was this validated externally (different hospital/population)?
- What's the improvement over current standard of care?
- Is this FDA-approved or in clinical guidelines?
- What's the false positive/negative rate trade-off?
- Can this integrate with my EHR (Epic, Cerner)?
- What's the implementation cost and workflow disruption?
For Patients
- Is this available now, or years away?
- Was this tested on people like me (age, ethnicity, condition)?
- Would my insurance cover this?
- Does this replace my current test, or add to it?
- What are the risks of false positives/negatives?
- Do major medical centers (Mayo, Cleveland Clinic) use this?
6. How We Curate
We use the S.I.G.M.A. framework to score research:
- Signal (20 pts): Study design, sample size, validation
- Impact (20 pts): Clinical significance, patient outcomes
- Governance (20 pts): Ethics, regulatory approval, transparency
- Maturity (20 pts): Readiness for clinical deployment
- Fit (20 pts): Alignment with evidence-based medicine
Score 80+: Likely breakthrough, practice-changing
Score 60-80: Promising, worth tracking
Score 40-60: Interesting, early-stage
Score <40: Not included in digest
Learn More
Questions? Email us at support@mednosis.com