MedTech companies are strong candidates for R&D tax relief because the sector is built on scientific and technological uncertainty. The UK’s clinical expertise, NHS reference environment and research strength create fertile conditions for qualifying R&D. At the same time, commercialisation challenges, slow adoption cycles and capital constraints mean companies often undertake substantial technical problem-solving, exactly the type of work HMRC recognises as eligible.
AI enabled diagnostics, surgical robotics, privacy preserving data systems, and NHS integration technologies all involve advancing science or technology and overcoming uncertainty, which places MedTech firmly within the scope of R&D tax incentives.
At the most basic level, for R&D tax purposes, development needs to satisfy two core tests:
MedTech goes wider and deeper than diagnostics reaching into most parts of the medical world. The Thames Valley in particular is a hotbed of activity due to the concentration of leading universities and specialist companies focused solely on development. It is a sector that ranks highly as a beneficiary of R&D tax relief. R&D is in the very fabric of the industry.
A typical claim is likely to include:
If you are involved in the MedTech world, or connected in some way to it, it is very likely that you will be in scope for R&D tax relief and also worth considering potential opportunities for Patent Box.
MedTech companies can typically claim for costs associated with:
All of these activities involve resolving scientific or technological uncertainty, which is central to HMRC’s definition of R&D.
Using a broad categorisation, there are five areas which R&D claims apply to in MedTech; staff costs, subcontractors, externally provided works (EPWs), consumables e.g. heat, light and power. You can find out more about how we use this data throughout your R&D tax claim below:
The majority of most R&D tax relief claims are based around staff costs. This includes people directly involved with the activity and others who are indirectly involved.
An example of direct costs would be a specific solutions architect – they tend to be defined, technical roles who are the doers of the project. A common misconception is they have to be scientists and academics, this is incorrect. If they are directly contributing to the R&D effort, then there is a good chance their costs can be included.
Indirect staff are people who are also involved in supporting the R&D project such as management, procurement, control functions, where they help enable the R&D to happen and also are part of the management of the process. These costs are usually overlooked but have a rightful place in the claim.
Where you may not have the expertise in house, you may rely on a third party to help with part of the R&D process. These costs are qualifying expenses for the R&D tax relief scheme. For overseas subcontractor costs new rules apply for accounting periods starting on or after 1 April 2024.
Like subcontractors, you may not have directly employed staff but want to have resource for a particular role who act in the same way as employees – i.e. under your care custody and control. These costs can form part of the claim. For overseas EPW’s costs new rules apply for accounting periods starting on or after 1 April 2024.
The proportion of heat, light and power that is used for the R&D project is also qualifying expenditure.
Any software used directly in the R&D is a qualifying cost. For accounting periods on or after 1 April 2023 costs related to datasets and cloud computing can also be claimed.
To qualify, MedTech work must aim to achieve an advance in science or technology and must involve overcoming uncertainty that competent professionals cannot easily resolve.
This highlights several areas where uncertainty is inherent:
There has been a raft of rule and rate changes that have impacted R&D tax relief. Our easy-to-use calculator will estimate your rate with answers to a few simple questions.
A client is a leader in the field of muscular analysis which helps create a cure plan and preventative measures to avoid repeat injury. Part of this process was the removal or reconstruction of muscle of tissue. Doctors wanted to understand how the muscle damage had changed or remained injured post death. Products in the market were able to slice the muscle but our client wanted to go even thinner to see if it would give any greater detail. They set about developing a new way of freezing and then slicing the muscle down to a thinness that had never been done before. This project continues but is a clear example of how growing technology on other sectors could potentially be used to create an advance in medicine.
A company builds an AI model to detect disease from imaging or biomarkers. The team must resolve uncertainties around accuracy, clinical validity, regulatory acceptance, and safe deployment.
An engineering group develops a robotic system with AI-driven navigation for minimally invasive surgery. They face uncertainties in precision, safety, real-time analytics, and integration with clinical workflows.
A MedTech firm designs a federated learning system that allows hospitals to train AI models without sharing raw patient data. Technical uncertainties include encryption, interoperability, and regulatory compliance.
A company creates a platform that automates compliance checks and accelerates procurement approvals. They must solve uncertainties around workflow automation, interoperability, and mapping to NHS procurement rules.
A spinout team converts an academic robotics prototype into a commercial surgical system. They face uncertainties in scaling, regulatory alignment, technical validation, and clinical evidence generation.
