Early-career researcher in LLM-as-a-judge uncertainty quantification and clinical AI scribe evaluation
3
papers here
1
reviewed by Evolve
1
first-author
1
corresponding
—
works · OpenAlex
0
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-17) 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—
Output is not yet scored: the OpenAlex snapshot provides only a single year of publication counts (2026: 5) with zero h-index and i10-index, so total works, venues, and multi-year output trajectory cannot be measured reliably yet.
Impact—
Impact is not yet scored: no citation counts beyond a 0 h-index and 0 i10-index are available in the snapshot, so this axis — measuring citations, uptake by later work, and influence on practice — cannot be estimated honestly.
Focus100/100
Focus scores 100 of 100. The five topic assignments collapse onto one cluster — Electronic Health Records Systems (3), Artificial Intelligence in Healthcare and Education (3), Topic Modeling (2), Machine Learning in Healthcare (2), Natural Language Processing Techniques (1) — and all three Evolve-reviewed papers sit on LLM-as-judge evaluation of clinical notes, with the field distribution listing machine learning / NLP (LLM-as-a-judge uncertainty quantification) as the sole field.
Collaboration28/100
Collaboration scores 28 of 100. The snapshot records 3 distinct coauthors, and the same three names — Sebastian Fox, Luke Markham, and Michael Karotsieris — recur as frequent coauthors, consistent with one small stable team behind the clinical scribe audits. The independent leadership signal sits on the uncertainty-decomposition paper, where Lail is first author and corresponding author, suggesting a separate line alongside the joint clinical work.
Activity52/100
Activity scores 52 of 100. The countsByYear series contains a single entry, 2026: 5, matching recent3yWorks = 5 — every recorded work appears in one year, with no visible output in earlier years. This is the signature of a compressed, all-at-once start rather than a steady cadence; the mid-range score reflects that burst against the absence of any prior-year baseline in the snapshot.
Reproducibility100/100
Reproducibility scores 100, computed as reproMean = 100 over 1 reproducibility-assessed paper — the first-authored uncertainty-decomposition work, rated high readiness. Among the reviewed papers, two carry high reproducibility readiness (the uncertainty decomposition and the omission-blindness benchmark) and one medium (the AI-scribe census, limited by anonymized products). The Evolve verdicts independently praise an exemplary instrument self-audit, consistent with the top score.
Research summary
Ryan Lail is an early-stage researcher at the intersection of machine learning and clinical natural language processing, focused on how large language models are evaluated. The OpenAlex record behind this profile lists 5 works in the last three years, all dated 2026, with an h-index and i10-index of 0 — a footprint consistent with a newly established researcher. Topic clustering is tight: Electronic Health Records Systems (3), Artificial Intelligence in Healthcare and Education (3), Topic Modeling (2), Machine Learning in Healthcare (2), and Natural Language Processing Techniques (1). Three Evolve-reviewed arXiv papers, all from 2026, set the agenda. As first and corresponding author, Lail proposed a closed-form aleatoric/epistemic decomposition of LLM-judge uncertainty to decide when to escalate to expert labels via BALD-style Bayesian acquisition; Evolve called the method honest but noted the real-data win reduces to label counts, tying a trivial baseline. Two co-authored papers audit deployed ambient AI scribes: a verified census finding that 31.3% of notes across three deployed systems carry a verified failure, and a benchmark showing LLM judges detect omissions near chance. Radar scores put focus and reproducibility at 100 — reproMean 100 over 1 assessed paper — with activity at 52 (5 recent works) and collaboration at 28 (3 distinct coauthors), sketching a small-team, tightly focused, early profile.
Collaboration pattern
Collaboration is compact: the snapshot records 3 distinct coauthors, and Sebastian Fox, Luke Markham, and Michael Karotsieris recur as the frequent-coauthor set, consistent with one small team producing the paired AI-scribe audits. Lail's independent signal is the first-authored, corresponding-author uncertainty-decomposition paper, which shares the LLM-as-judge theme but no listed coauthors from the clinical trio. Radar's collaboration score of 28 reflects this small, early network rather than absence of collaboration.
Research keywords
Highlights
Closed-form aleatoric/epistemic split for LLM-judge escalation
One note in three carries a verified AI-scribe failure
LLM judges verify presence, not absence
Timeline
- 2026First-authored and corresponding paper proposing a closed-form aleatoric/epistemic split of LLM-judge uncertainty to target expert labels (arXiv).
- 2026Co-authored a verified census of three deployed ambient AI scribes, finding 31.3% of notes carry a verified failure.
- 2026Co-authored the omission-blindness benchmark showing LLM judges verify presence, not absence, in AI clinical notes.
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.
- Bibliometric figures come from an OpenAlex record only (h-index 0, i10-index 0, countsByYear limited to 2026); no other citation source informs this profile.
- No institution, country, or ORCID was available; identity is name-resolved from public bibliographic data and may conflate researchers sharing this name.
- Output and impact dimensions are unscored: the snapshot lacks multi-year work counts and any positive citation evidence.
- Qualitative assessments rest on three Evolve-reviewed arXiv papers (all 2026); the reproducibility score draws on a single assessed paper (reproPapers = 1).