FigureAsia 35 Under 35 · Healthcare
Dani Kiyasseh
Age 30 · Surgical video intelligence · London and New York
Researcher-founder building large-scale surgical-video models and a 2026 dataset intended to represent operative actions across procedures.
- Approximate age at 31 December 2025
- 30
- Field
- Healthcare
- Country or region
- London and New York
- FigureAsia U35 Assessment
- 83.0 / 100
Profile
Career and documented record
Operating rooms produce rich video, yet the data are fragmented, sensitive and difficult to label. Dani Kiyasseh has built a research programme around converting that record into structured evidence about procedural phases, actions and performance. In 2025 he founded Halsted AI, and in 2026 the team released research describing HSA-27k, a surgical action dataset, alongside a broader effort to map procedures at scale.
Organization materials describe access to hundreds of thousands of surgical videos, but those figures and associated performance claims are company-reported. The scientific contribution should be judged by the released methodology, dataset documentation and Kiyasseh's established publication record, not by the size of a private archive alone.
Surgical AI carries unusual governance demands. Consent, de-identification, institution bias, surgeon surveillance and the temptation to convert a model score into a competence judgment all require explicit safeguards. Kiyasseh's inclusion recognizes technical leadership and the potential to make surgery more measurable, while treating clinical deployment and outcome improvement as questions still to be answered.
FigureAsia selection
Why Dani Kiyasseh is on the list
FigureAsia selected Kiyasseh because surgical intelligence needs people who understand both the modeling problem and its governance burden. His 2025–2026 work has moved from academic research into a dedicated company and a more systematic action taxonomy. The score is constrained by preprint maturity and organization-reported scale, but his individual agency and the international significance of the problem are clear.
Verified work
The 2025–26 record
Principal milestone
Company founded in 2025
Evidence record
HSA-27k surgical-action dataset described in 2026
Scale or implementation
Organization-reported archive exceeding 650,000 surgical videos
Field context
The work in its field
Within surgical video intelligence, the relevant test is whether a result can survive scrutiny of maturity, attribution, validity and practical fit. That distinction matters: completed evidence is not projected benefit, and individual responsibility is not interchangeable with the wider team’s achievement.
FigureAsia U35 Assessment
Assessment breakdown
83.0out of 100
Substantive 2025–2026 contribution
18 / 20
The score reflects completed 2025–26 work in surgical video intelligence, assessed at the documented maturity of research dataset and early commercial development.
Verified impact
10.5 / 15
Impact credit is limited to the measured study, regulatory, implementation or operating record stated in the profile; unsupported patient benefit is excluded.
Originality and distinction
9 / 10
The work creates or materially advances a distinctive capability within surgical video intelligence rather than relying on title or institutional association.
Field and industry influence
8 / 10
The assessment recognises demonstrated effects on research, product development, care delivery or professional practice, with publicity alone carrying no weight.
Individual agency
10 / 10
Named authorship and the documented role of Founder and Chief Executive Officer establish individual responsibility while preserving credit for collaborators.
Durability and trajectory
4.5 / 5
The cited work forms part of a continuing programme, platform or research trajectory rather than a single uncompleted announcement.
Asian significance and global relevance
5 / 5
The Asian connection is material to the person's identity, operating base or populations served: Syrian-American machine-learning researcher.
Clinical and scientific validity
4.9 / 7
Clinical and scientific validity is calibrated to research dataset and early commercial development, with the profile retaining the evidence boundary attached to the result.
Safety, quality and responsible governance
4.9 / 7
Safety and governance credit reflects accurate regulatory language, study limitations, data stewardship and the refusal to turn early evidence into clinical certainty.
Translation and care-pathway fit
4.2 / 6
The work is scored for its demonstrated fit with a laboratory, regulatory, clinical, operational or public-health pathway, not for projected future adoption.
Access, equity and resource stewardship
4 / 5
Access credit reflects documented reach, capacity, affordability or inclusion while distinguishing service volume from proven clinical outcome.