FigureAsia 35 Under 35 · Healthcare
Mohanad Alkhodari
Age 31 · Hypertension, physiological signals and cardiovascular AI · Oxford, United Kingdom
Lead researcher on HyperScore, a 2026 method that derived six hypertension trajectories from large population datasets and added risk information beyond a single blood-pressure reading.
- Approximate age at 31 December 2025
- 31
- Field
- Healthcare
- Country or region
- Oxford, United Kingdom
- FigureAsia U35 Assessment
- 89.0 / 100
Profile
Career and documented record
Hypertension is usually summarized as two numbers. Mohanad Alkhodari's work asks what is lost when the waveform itself is discarded. In 2026, research highlighted by his Oxford institute described HyperScore, an artificial-intelligence method that interprets the morphology of a blood-pressure signal to identify patterns associated with hidden organ damage and future cardiovascular risk.
The analysis drew on approximately 27,000 participants from UK Biobank and a further 5,500 from the ARIC cohort. It produced six physiological trajectories and tested whether they added information beyond conventional pressure values. That external-cohort structure is important: it moves the work beyond a single-dataset model and toward a reproducible biomarker question.
Alkhodari's path links Palestine, engineering training in the UAE and cardiovascular research in Oxford. His agency is established through the sustained signal-processing programme and lead role in the cited work. HyperScore is not a licensed diagnostic, and prospective evidence that using it changes treatment or outcomes is not yet available. Its contribution is to turn a familiar measurement into a richer, potentially more discriminating account of vascular stress.
FigureAsia selection
Why Mohanad Alkhodari is on the list
FigureAsia selected Alkhodari because he has combined a globally common clinical signal with large-cohort validation and a clear route to implementation. The work is relevant to health systems that cannot add expensive imaging to every hypertension visit. His score reflects the scale and external validation of the research, while the profile stops short of claiming clinical utility before prospective deployment and outcome studies exist.
Verified work
The 2025–26 record
Principal milestone
Approximately 27,000 UK Biobank participants
Evidence record
Approximately 5,500 participants in external ARIC data
Scale or implementation
Six physiological hypertension trajectories
Field context
The work in its field
Within hypertension, physiological signals and cardiovascular ai, 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
89.0out of 100
Substantive 2025–2026 contribution
18 / 20
The score reflects completed 2025–26 work in hypertension, physiological signals and cardiovascular ai, assessed at the documented maturity of large-cohort translational biomarker research.
Verified impact
13.5 / 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 hypertension, physiological signals and cardiovascular ai 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 Postdoctoral Researcher in Cardiovascular Medicine establish individual responsibility while preserving credit for collaborators.
Durability and trajectory
4 / 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: Palestinian researcher educated in the United Arab Emirates and working in the United Kingdom.
Clinical and scientific validity
6.3 / 7
Clinical and scientific validity is calibrated to large-cohort translational biomarker research, with the profile retaining the evidence boundary attached to the result.
Safety, quality and responsible governance
6.3 / 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
5.4 / 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 / 5
Access credit reflects documented reach, capacity, affordability or inclusion while distinguishing service volume from proven clinical outcome.