Machine learning for healthcare researcher working on medical AI safety, privacy, and angiographic image analysis
2
papers here
1
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-09-20) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Not yet computed: this axis measures publication volume and productivity over time — papers per year, venue tier, and author-role counts. The current snapshot holds only two catalogued 2026 arXiv preprints, too thin to score.
Impact—
Not yet computed: this axis would aggregate citations, field-weighted citation impact, and influence indicators. No citation data was supplied for this profile, so no score is reported or estimated here.
Focus100/100
Scores 100 of 100, reflecting complete concentration in Evolve's field classification: the only catalogued paper with a field label falls under machine learning for healthcare with a medical AI safety emphasis. The other 2026 work — graph-based longitudinal angiographic image analysis — sits adjacent in medical imaging. Because the classified base is a single paper, this perfect score should be read as early-snapshot concentration rather than a long-run record.
Collaboration48/100
Scores 48 of 100 on Evolve's 0-100 collaboration scale, computed from 10 distinct co-authors in the snapshot. The pattern is a compact, recurring cluster: Martin J. Menten, Laurin Lux, Moritz A. Knolle, Mélanie Roschewitz, Emma A. M. Stanley, Georgios Kaissis, Ben Glocker, Linus Kreitner, and Carmen Baumann. Both catalogued papers list the researcher in a middle, non-corresponding position, consistent with contributing within team-driven medical AI projects rather than solo work.
Activity—
Not yet computed: this axis tracks publishing cadence and research tempo — recent-year output, intervals between works, and ongoing projects. Only two 2026 papers are available, which cannot establish an activity trend.
Reproducibility100/100
Scores 100 of 100, computed from a reproducibility mean of 100 across one assessed paper: 'Memorisation bias in medical AI', which Evolve rates high for reproducibility readiness, consistent with its rigorous large-scale design across ECG and chest radiograph modalities. The second catalogued paper has not been assessed, so this perfect score rests on a single-paper base and may move as more works are reviewed.
Research summary
Daniel Rueckert is profiled here from public bibliographic data together with the papers Evolve has reviewed; the current snapshot is intentionally narrow. Two 2026 arXiv works anchor it. 'Memorisation bias in medical AI' sits in machine learning for healthcare with a medical AI safety emphasis: Evolve's review notes it claims that training on patients' past records biases AI predictions on their future data, supported by rigorous large-scale evidence spanning ECG deep learning and chest radiograph classification, while judging the clinical harm modest; its reproducibility readiness is rated high. 'Temporally Consistent Graph Extraction and Matching for Longitudinal Angiographic Images' extends the portfolio toward graph-based longitudinal vascular image analysis. On both papers the researcher holds a middle author position without corresponding role, consistent with a collaborative, contributing style. Radar scores give focus and reproducibility 100 each and collaboration 48 across 10 distinct co-authors; output, impact, and activity lack sufficient data. Recurring collaborators include Martin J. Menten, Laurin Lux, Georgios Kaissis, and Ben Glocker.
Collaboration pattern
Collaboration is team-based and recurring: 10 distinct co-authors appear in the snapshot, with Martin J. Menten, Laurin Lux, Moritz A. Knolle, Mélanie Roschewitz, Emma A. M. Stanley, Georgios Kaissis, Ben Glocker, Linus Kreitner, and Carmen Baumann as frequent names. On both catalogued 2026 papers the researcher is a middle author, not corresponding, suggesting contribution within group-led medical AI efforts rather than solo-author projects.
Research keywords
Highlights
Large-scale evidence that memorisation biases medical AI predictions
Temporally consistent graph methods for longitudinal angiographic images
A 2026 arXiv paper on graph extraction and matching for longitudinal angiographic images, with the researcher as a middle author; it broadens the portfolio into longitudinal vascular image analysis, though Evolve has not yet issued a verdict, field label, keywords, or reproducibility assessment.
High reproducibility rating on the first reviewed paper
Timeline
- 2026Publishes 'Temporally Consistent Graph Extraction and Matching for Longitudinal Angiographic Images' on arXiv as a middle author, contributing graph-based methods for longitudinal vascular image analysis.
- 2026Publishes 'Memorisation bias in medical AI' on arXiv as a middle author; Evolve reviews it, citing rigorous large-scale evidence on memorisation bias across ECG and chest radiograph tasks and rating reproducibility readiness high.
- 2026Both catalogued works fall in 2026, spanning medical AI safety and medical image analysis — they form the entire basis of this profile's current snapshot, scores, and caveats.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- No institution, country, or ORCID was supplied; the profile is name-resolved from public bibliographic data and may require identity disambiguation.
- Bibliometrics come only from the supplied snapshot (two 2026 arXiv papers); output, impact, and activity dimensions are uncomputed for lack of data.
- The focus and reproducibility scores of 100 rest on a single field-classified paper and a single reproducibility-assessed paper respectively.
- The 2026 angiographic paper has no Evolve verdict, field label, keywords, or reproducibility assessment yet.