Scientific Advisory & Responsible Innovation
Scientific advisory, research ethics, and responsible health data intelligence
Longitudinal health information creates valuable opportunities for research. It also creates responsibilities concerning scientific validity, patient privacy, transparency and the appropriate use of sensitive information.
Clarrio.ai approaches these responsibilities through sound scientific principles and appropriate research governance.
Scientific advisory
Scientific advisory input for health data research
Clinical and pharmaceutical expertise informs decisions well before any data is collected.
- 01
Research strategy
Which questions longitudinal data can credibly answer, and which it cannot.
- 02
Therapeutic-area relevance
What matters to patients and clinicians in each condition, from seizure diaries to cardiometabolic markers.
- 03
Study methodology
Observational designs, sampling frequency, comparators and handling of missing data.
- 04
Endpoint selection
Choosing measures that are meaningful, feasible to collect and interpretable over time.
- 05
Longitudinal interpretation
Reading trajectories with attention to seasonality, device change and engagement effects.
- 06
PRO selection
Preferring validated instruments and appropriate recall periods for patient-reported outcomes.
- 07
Application review
Assessing whether a proposed research use fits the data, the consent and the purpose.
A scientific advisor directory will be published once appointments are confirmed.
Principles
Our principles of responsible research
Respect for patients
Health data collection and use should respect patient rights, appropriate consent requirements and applicable legal obligations.
Scientific validity
Research conclusions must reflect the quality, limitations and context of the underlying information.
Privacy and confidentiality
Sensitive health information requires appropriate organizational and technical safeguards.
Fairness and representation
Research must consider population differences, missing information and potential sources of bias.
Human accountability
AI-enabled analysis must remain subject to appropriate human oversight and professional interpretation.
Transparency
Research methods, assumptions, limitations and data-processing purposes should be communicated clearly.
Ethics oversight
Independent review where research requires it
Research using health data generally needs approval from an independent ethics committee or institutional review board, under the rules of each country and institution involved. These approvals are protocol-specific: they cover a defined question, population, consent process and data flow.
In sponsor- or institution-led studies, those approvals are obtained by the sponsor or institution, and Clarrio.ai operates within the approved protocol. Program analytics outside a research protocol follow contractual purpose limits and applicable data protection law.
Clarrio.ai does not present an internal ethics committee as operational. If one is established, its mandate, members, review procedures and conflict-of-interest principles will be published here.
Responsible AI
AI should support scientific understanding, not replace scientific judgment
Transparency
Summaries link back to the underlying observations, so a reviewer can see what a statement is based on.
Human review
Outputs support professionals; they do not make clinical decisions. Interpretation stays with qualified people.
Data quality first
Gaps, device changes and low engagement are surfaced rather than smoothed over, because they change what can be concluded.
Stated limitations
Associations in observational data are described as associations. No autonomous diagnosis, validated prediction or automated treatment decision is implied.
Appropriate controls
Access to analysis is restricted by role and purpose.