FigureAsia 35 Under 35 · Healthcare
Yueming Jin
Age 31 · Medical imaging and surgical intelligence · Singapore
Young Singapore-based laboratory leader behind a 2025 segmentation system evaluated across 17 tasks and further work on surgical reasoning and workflow anticipation.
- Approximate age at 31 December 2025
- 31
- Field
- Healthcare
- Country or region
- Singapore
- FigureAsia U35 Assessment
- 82.4 / 100
Profile
Career and documented record
Yueming Jin's 2025 research moved across three levels of medical intelligence: seeing anatomy, reasoning about a surgical scene and anticipating what comes next. The Med-SA work evaluated a medical-image segmentation approach across 17 tasks, testing whether a single framework could adapt across varied anatomy and imaging settings. Her laboratory also led work on surgical reasoning and workflow anticipation, where the value of a model depends on temporal context rather than a single frame.
The programme has attracted substantial research use; institutional records report more than 800 citations and over 1,200 GitHub stars across her work. Those cumulative metrics are contextual, not direct clinical outcomes. Jin's agency is established through laboratory leadership, senior authorship and an independent faculty appointment spanning biomedical engineering and computing.
No cited system is represented as a cleared surgical device. Benchmark breadth and open-source adoption do not show that an operating room became safer or faster. The contribution is the construction of generalizable technical foundations for clinicians and engineers to test, with a clear Asia-based centre in Singapore.
FigureAsia selection
Why Yueming Jin is on the list
FigureAsia selected Jin because her work reaches beyond a narrow image benchmark toward the sequence and reasoning demands of surgery. The 17-task evaluation and visible research adoption give the programme influence, while the ranking remains conservative on translation. Her profile illustrates why healthcare AI must be judged by task breadth, workflow fit and honest maturity — not by a single accuracy number.
Verified work
The 2025–26 record
Principal milestone
17 medical-image segmentation tasks evaluated
Evidence record
More than 1,200 GitHub stars reported across the research programme
Scale or implementation
More than 800 citations reported by the institution
Field context
The work in its field
Within medical imaging and surgical 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
82.4out of 100
Substantive 2025–2026 contribution
18 / 20
The score reflects completed 2025–26 work in medical imaging and surgical intelligence, assessed at the documented maturity of research validation across imaging and surgical tasks.
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 medical imaging and surgical 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
9 / 10
Named authorship and the documented role of Assistant Professor of Biomedical Engineering and Electrical and Computer Engineering 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
4.5 / 5
The Asian connection is material to the person's identity, operating base or populations served: Chinese-educated engineer with a doctorate from Hong Kong and a faculty laboratory in Singapore.
Clinical and scientific validity
5.6 / 7
Clinical and scientific validity is calibrated to research validation across imaging and surgical tasks, with the profile retaining the evidence boundary attached to the result.
Safety, quality and responsible governance
5.6 / 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
3.5 / 5
Access credit reflects documented reach, capacity, affordability or inclusion while distinguishing service volume from proven clinical outcome.