Contents
Of everything promised about artificial intelligence in medicine, one case has completed the full course: randomised trial, prespecified primary endpoint, published result. It is breast cancer screening, and the trial is called MASAI. With 105,934 women randomised within Sweden’s national programme, AI-supported reading detected 29% more cancers than standard double reading, without increasing false positives, and cut reading workload by 44%. And in 2026 came the missing figure: interval cancers, the endpoint that actually measures whether a screening programme is safe. This article explains why this case counts and what sets it apart.
The 30-second summary: the seven figures that matter
Each row names the study and the year. Note that the first three are successive publications from the same trial.
| Finding | Result | Study and year |
|---|---|---|
| Cancers detected per 1,000 women screened | 6.4 against 5.0; ratio of 1.29 (95% CI: 1.09-1.51; p = 0.0021) | Hernström et al., Lancet Digital Health (2025) |
| False positives | No increase: ratio of 1.01 (95% CI: 0.91-1.11; p = 0.92) | Hernström et al., Lancet Digital Health (2025) |
| Radiologist reading workload | 44.2% fewer reads | Hernström et al., Lancet Digital Health (2025) |
| Interval cancers, the primary endpoint | 1.55 against 1.76 per 1,000; ratio of 0.88, non-inferior | Gommers et al., The Lancet (2026) |
| Screening sensitivity | 80.5% against 73.8% (p = 0.031), at the same specificity | Gommers et al., The Lancet (2026) |
| Real-world rollout in Germany, 463,094 women | 6.7 against 5.7 per 1,000: 17.6% more detection | Eisemann et al., Nature Medicine (2025) |
| Reading reduction across 31 reviewed studies | Between 40% and 90% with triage strategies | Jassim et al., BMJ Open (2025) |
What you will find here
- How MASAI is designed and why its design differs from the rest.
- What an interval cancer is and why it was the missing figure.
• What happened when it was taken to 463,094 women outside a trial.
- What the systematic review says is still unresolved.
The trial: how it was done
MASAI ran within Sweden’s national screening programme, at four centres in the south-west of the country. Between April 2021 and December 2022, 105,934 women were randomly assigned 1:1 to AI-supported screening or to standard double reading without AI. Median age was 53.7. Allocation was made automatically by the image archiving system after acquisition, and the radiologists doing the reading did not know which group each case belonged to.
The tool is used in two ways, and it is worth distinguishing them because that is where the result comes from. The system assigns each examination a risk score on a ten-level scale, used to decide whether that examination goes through one reading or two; and it also marks suspicious findings for the reading radiologist. At no point does it replace anyone: it reorganises the work and it points.
The detection results, published in Lancet Digital Health in 2025: 6.4 cancers per 1,000 women screened in the AI group against 5.0 in the control group, a ratio of 1.29 (95% CI: 1.09-1.51; p = 0.0021).
What other types of cancer were identified
This question is what separates a useful screening programme from one that merely generates diagnoses. The additional detection was mostly of small invasive cancers without nodal involvement: 58 more T1 tumours, 46 more lymph-node negative, and 21 more of subtypes other than luminal A. In situ carcinomas also increased, and about half of that increase was high grade.
And what did not happen: neither the recall rate nor the false-positive rate rose significantly, while the positive predictive value of recall improved with a ratio of 1.19 (95% CI: 1.04-1.37; p = 0.012). Reading workload fell from 109,692 to 61,248 reads: 44.2% fewer.
| Study | What it contributed | Result | What it cannot support |
|---|---|---|---|
| Lång et al., Lancet Oncology (2023) | Safety analysis with the first 80,033 women | Similar or higher detection; 44.3% fewer reads | An interim safety analysis, not the final endpoint |
| Hernström et al., Lancet Digital Health (2025) | Secondary outcomes with the full sample | 29% more detection without more false positives | Detection is not the same as patient benefit |
| Gommers et al., The Lancet (2026) | Primary endpoint: interval cancer | Non-inferior, with higher sensitivity and equal specificity | It does not measure mortality; follow-up is two years |
| Eisemann et al., Nature Medicine (2025) | Real-world rollout at 12 German centres | 17.6% more detection without worsening recall | Radiologists voluntarily chose whether to use the tool |
| Jassim et al., BMJ Open (2025) | Review of 31 studies and over 2 million examinations | Reading reduction of 40% to 90% with triage | Interval-cancer follow-up incomplete in several datasets |
The missing figure: interval cancers
Identifying a higher volume of cancers doesn’t automatically prove the superiority of a screening initiative. An initiative can boost its detection stats by spotting growths that would have otherwise remained harmless, all while failing to catch the fast-moving ones. The metric designed to capture this flaw is the interval cancer: precisely the one surfacing in the gap between successive screening sessions, meaning the one the process failed to spot.
That constituted the main goal of MASAI, featured in The Lancet in 2026, utilizing a predefined non-inferiority margin of 20%.
| Metric | With AI | Standard double reading |
|---|---|---|
| Interval cancers per 1,000 participants | 1.55 (95% CI: 1.23-1.92) | 1.76 (95% CI: 1.42-2.15) |
| Proportion ratio | 0.88 (95% CI: 0.65-1.18) | p = 0.41: non-inferior |
| Invasive interval cancers | 75 | 89 |
| Interval cancers at stage T2 or above | 38 | 48 |
| Sensitivity | 80.5% (76.4-84.2) | 73.8% (68.9-78.3); p = 0.031 |
| Specificity | 98.5% | 98.5%; p = 0.88 |
The outcome fulfills the criteria while delivering an extra advantage. Beyond the complete absence of extra interval cancers, cases arising within the AI cohort generally exhibited reduced invasiveness and earlier stages, whereas screening sensitivity climbed by almost seven points without sacrificing a single tenth of a point in specificity. This outcome is both definitive and unusually hard to achieve in this domain.
What happens outside the trial
A clinical trial is a controlled environment. The next question is whether this holds up in a real programme, and there is a sizeable answer. The PRAIM study, published in Nature Medicine in 2025, followed 463,094 women aged 50 to 69 screened at 12 German centres between July 2021 and February 2023, read by 119 radiologists.
The detection rate in the AI-supported group was 6.7 per 1,000 against 5.7 in the control group: 17.6% more (95% CI: 5.7% to 30.8%), statistically superior. The recall rate was slightly lower and non-inferior, and the positive predictive value rose from 14.9% to 17.9%.
There is one restriction that the researchers themselves point out and which must not be overlooked: radiologists voluntarily chose whether to use the system, meaning this is not a randomized trial and disparities might exist between the group that utilized it and the one that did not.
What the systematic review qualifies
The broadest synthesis available was published by Ghufran Jassim and colleagues in BMJ Open in 2025: 31 studies covering more than two million screening examinations across Europe, Asia, North America and Australia.
Their conclusions match the above on the essentials. As a second reader or within double-reading workflows, AI maintains or modestly increases sensitivity — by up to nine percentage points — while preserving or improving specificity; and triage configurations reduce reading volume by between 40% and 90% while maintaining non-inferior detection when thresholds are conservatively calibrated.
However, the authors raise a pair of caveats. The initial point involves general reliability: variations among methodologies alongside reliance on selected populations restrict certain precision measures, while follow-up data regarding interval cancers remains partial across several key investigations. The subsequent point addresses independent deployment: clear documentation, uniform assessment frameworks, and extended demographic research must be established prior to evaluating artificial intelligence as an independent diagnostician.
Practical applications for this
Ordered by the strength of the evidence behind it:
• Always ask for the interval-cancer figure. It represents the critical metric that distinguishes finding more from finding better, and the exact data MASAI delayed publishing for three years.
• The role that is demonstrated is triage and support, not sole reader. That is what the trial evaluated and what the systematic review considers safe today.
- Examine the supplementary type of tumour identified. Within MASAI, compact invasive malignancies lacking lymph node involvement predominated, perfectly fulfilling the core objective of screening.
• Check that recall does not rise. The value of the result lies in detection increasing at no cost in false positives.
- Distinguish trial from rollout. The German study featuring 463,094 women boasts a massive scale, yet adoption of the tool remained voluntary, lacking true random assignment.
Frequently asked questions
Can artificial intelligence identify a higher number of breast cancers than radiologists?
In supported screening, indeed, this is quantified via a randomised trial. Within MASAI, encompassing 105,934 women derived from Sweden’s national initiative, the detection rate reached 6.4 malignancies per 1,000 evaluations utilising AI, compared to 5.0 through conventional double reading, yielding a ratio of 1.29 (95% CI: 1.09-1.51; p = 0.0021). The artificial intelligence system did not operate autonomously; rather, it triaged examinations and highlighted suspicious areas for the specialist.
Do false positives increase?
Not significantly. Within that very same trial, the false-positive ratio stood at 1.01 (95% CI: 0.91-1.11; p = 0.92) alongside a recall ratio of 1.08 (0.99-1.17; p = 0.084), whereas the positive predictive value concerning recall saw an enhancement with a ratio reaching 1.19 (p = 0.012).
What is an interval cancer and why does it matter so much?
It represents the malignancy developing between consecutive screening intervals, specifically the one missed by the initial examination. Serving as the core objective for MASAI, the findings shared in The Lancet in 2026 indicated 1.55 cases per 1,000 using artificial intelligence compared to 1.76 without it, yielding a proportion of 0.88 (95% CI: 0.65-1.18) that successfully satisfied the non-inferiority benchmark. Absent this metric, merely identifying a higher volume of tumors fails to demonstrate superior screening efficacy.
How many hours of labor does it eliminate?
Around half the reads. MASAI had 61,248 reads in the AI group against 109,692 in the control group, 44.2% fewer, and the review of 31 studies puts the reduction between 40% and 90% depending on the triage strategy used.
Is it possible for artificial intelligence to independently read mammograms?
Not yet, on the available evidence. The systematic review published in BMJ Open in 2025 concludes that transparent reporting, standardised evaluation and long-term population studies are needed before considering AI as a stand-alone reader, and notes that interval-cancer follow-up is incomplete in several of the available datasets.
What we do not know
• Whether it reduces mortality. No study has measured it: the hardest endpoint available so far is interval cancer at two years.
• What happens across several screening rounds. The published follow-up covers one interval, not a lifetime of successive screens.
- Whether the outcome relies on the specific setup. MASAI utilized a distinct release of a commercial software, and comparative, randomised evaluations between platforms are absent.
• How it behaves in different populations. The large studies are Swedish, Danish and German, with organised screening programmes and relatively homogeneous populations.
References
1. Hernström, V., Josefsson, V. et al. Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI). The Lancet Digital Health, 2025. DOI: 10.1016/s2589-7500(24)00267-x
2. Gommers, J., Hernström, V. et al. Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study. The Lancet, 2026. DOI: 10.1016/s0140-6736(25)02464-x
3. Lång, K., Josefsson, V. et al. Artificial intelligence-supported screen reading versus standard double reading in the MASAI trial: a clinical safety analysis. The Lancet Oncology, 2023. DOI: 10.1016/s1470-2045(23)00298-x
4. Eisemann, N., Bunk, S. et al. Nationwide real-world implementation of AI for cancer detection in population-based mammography screening. Nature Medicine, 2025. DOI: 10.1038/s41591-024-03408-6
5. Jassim, G., Otoom, O. et al. Performance of artificial intelligence in breast cancer screening programmes: a systematic review. BMJ Open, 2025. DOI: 10.1136/bmjopen-2025-111360
This material is purely informational and should not be considered medical guidance. The outcomes detailed originate from structured screening initiatives employing dedicated systems and protocols, meaning they cannot be applied directly to alternative clinical environments or personal choices regarding diagnostic procedures.
