Regulatory & clinical glossary

Software intended to be used for medical purposes without being part of a hardware medical device. SaMDs perform functions such as diagnosis, prognosis, treatment, or monitoring, and are subject to full medical device regulatory frameworks including CE MDR and FDA oversight.

The European Union regulatory framework governing the safety, performance, and market access of medical devices, including Software as a Medical Device. CE MDR replaced the older MDD in May 2021 and introduced stricter requirements for clinical evidence, post-market surveillance, and conformity assessment.

A medium-risk category of medical device under EU MDR. Class IIa devices require assessment by a Notified Body and must demonstrate clinical evidence of safety and performance. Most SaMDs involved in treatment support or monitoring fall in Class IIa or higher.

The German regulatory pathway for digital health applications, established under the Digital Healthcare Act (DVG). DiGA-approved apps can be prescribed by physicians and reimbursed by statutory health insurers in Germany, making DiGA one of the most advanced national frameworks for integrating DTx into routine clinical care.

The systematic process of collecting and analysing data on the safety and performance of a medical device after it has been placed on the market. PMS is a mandatory requirement under EU MDR and includes vigilance reporting, periodic safety update reports (PSURs), and ongoing clinical follow-up.

The international standard specifying lifecycle requirements for medical device software development. It defines processes for software planning, design, implementation, testing, maintenance, and risk management. Compliance with IEC 62304 is required for CE MDR and FDA software submissions.

The international quality management system standard specifically designed for medical device manufacturers. ISO 13485 certification demonstrates that an organisation consistently designs and manufactures medical devices that meet regulatory and customer requirements.

The international standard for risk management in medical devices. It defines a systematic process for identifying hazards, estimating and evaluating risks, controlling risks, and monitoring the effectiveness of controls throughout the device lifecycle.

The international standard for Information Security Management Systems (ISMS). In digital health, ISO 27001 certification demonstrates that an organisation has implemented systematic controls to protect sensitive patient data and clinical information assets.

The European Union regulation governing the collection, processing, and storage of personal data. In digital health, GDPR compliance requires explicit consent for processing health data (a special category under Article 9), data minimisation, and the right to erasure.

The US federal law establishing standards for the protection of individually identifiable health information (PHI). HIPAA compliance is required for any organisation handling PHI in the US healthcare system and involves administrative, physical, and technical safeguards for data security.

Evidence-based software interventions that prevent, manage, or treat medical conditions. DTx products deliver clinical-grade therapeutic interventions directly to patients and are distinguished from general wellness apps by rigorous clinical validation and, typically, regulatory clearance.

A measurable indicator of a biological or physiological state captured through a digital device — such as a wearable sensor, smartphone, or app — rather than a traditional clinical test. Digital biomarkers enable continuous, real-world monitoring of disease progression, treatment response, and patient behaviour.

Chronic medical conditions that are not transmitted from person to person, including cardiovascular disease, diabetes, cancer, chronic respiratory disease, and neurological disorders. NCDs account for 74% of global deaths and represent the primary disease burden that SaMDs and digital therapeutics are designed to address.

The clinical process of adjusting the dose and timing of levodopa — the primary pharmacological treatment for Parkinson's disease — to maximise therapeutic ON-state duration and minimise motor fluctuations. AI-assisted levodopa optimisation uses continuous wearable sensor data and patient-reported outcomes to guide personalised dose adjustments.

A structured, evidence-based psychological treatment that addresses maladaptive patterns of thinking and behaviour. In digital health, CBT principles are delivered through apps and VR environments to treat conditions such as chronic pain, anxiety, and depression at scale without requiring in-person sessions.

Clinical evidence derived from data collected outside traditional randomised controlled trials, including electronic health records, wearable sensors, patient registries, and digital health platforms. RWE is increasingly accepted by regulators to support product approvals, label expansions, and post-market commitments.

The application of psychology, behavioural economics, and behaviour change theory to the design of digital health interventions. Evidence-based techniques such as habit formation, motivational interviewing, and self-monitoring are embedded in SaMDs to improve patient engagement and long-term adherence.

Newel Health's proprietary AI development platform for building certified Software as Medical Devices. H.Core integrates a regulatory-compliant quality management system, a pre-qualified clinical module library, a behavioural science engine, and an AI/ML layer trained on real-world clinical data.

The natural or legal person responsible for the design, manufacture, packaging, and labelling of a medical device under their own name, and who assumes full regulatory accountability for that device. For SaMDs developed in partnership, the legal manufacturer takes responsibility for conformity assessment, CE marking, and post-market surveillance.

Artificial intelligence systems designed so that their outputs and decision processes can be understood and interpreted by human experts. In clinical AI, explainability is a regulatory and ethical requirement: clinicians must be able to understand why an AI system produced a given recommendation before acting on it.