MedTech

Revolutionizing Healthcare: The Rise of AR and VR in MedTech

Augmented Reality (AR) and Virtual Reality (VR) are revolutionizing medical devices and healthcare delivery. AR technologies are increasing surgical efficiency and safety by enabling real-time 3D visuals and integrated patient data. AR is also enhancing remote consultations by overlaying diagnostics during telemedicine visits. Meanwhile, VR is revolutionizing medical training through platforms that enable surgeons to practice procedures in a risk-free, simulated environment, leading to performance improvements of over 230 percent compared to traditional training methods. In rehabilitation, VR engages patients with interactive, motion-based therapy, accelerating recovery and boosting adherence to rehabilitation programs. VR is also proving effective in pain management, offering distraction during treatments such as burn care and labor pain management.

The adoption of AR/VR in MedTech has accelerated rapidly. As of September 6, 2024, FDA has authorized 69 AR/VR-based medical devices for US marketing, with nearly 80% concentrated in radiology and orthopedics. This marks a significant uptick since the first FDA approval in 2015. Between 2019 and 2023, the AR/VR medical device market achieved an extraordinary 68% compound annual growth rate, a trend expected to continue as these technologies are further deployed in surgical planning, diagnostics and patient care management.

As AR/VR becomes increasingly integrated into healthcare, the collection of substantial amounts of personal data has the potential to raise significant privacy concerns, particularly if this data is not adequately secured. Users have also raised potential concerns with respect to personal autonomy, highlighting the need for AR/VR device companies to implement data protection policies that clearly outline the types of data being collected and define users’ rights to manage and restrict usage of such data. While other challenges such as cost, limited reimbursement and the need for specialized training to deploy such technologies remain, the transformative potential of AR and VR continues to reshape healthcare, driving substantial gains in precision, efficiency and patient outcomes.

The Role of AI Assurance Labs in Safeguarding Healthcare Innovation

As artificial intelligence (AI) transforms healthcare, ensuring the safety, accuracy, and fairness of AI models has become a critical regulatory focus. The UK government’s newly launched AI assurance platform aims to bolster its AI sector and establish reliable testing frameworks. Designed to mitigate risks like biases and security vulnerabilities, the platform empowers businesses to deploy AI responsibly while complying with ethical and legal standards.

At the Consortium for Healthcare AI Global Summit in October, 2024, industry leaders, researchers, and regulators discussed creating a national network of AI assurance labs across the US. Such labs would validate AI models prior to FDA review, serving as a vital checkpoint for systems that assist in diagnosing diseases, recommending treatments, and various other clinical applications. AI models, driven by machine learning, evolve based on the data they process, posing unique validation challenges compared to traditional medical technologies. By rigorously testing models for reliability, fairness, and compliance with FDA and American Medical Association standards, AI assurance labs could streamline approvals and mitigate deployment risks.

Critics of the proposed AI assurance labs initiative in the US have raised several concerns around intellectual property, data confidentiality, and potential conflicts of interest—particularly if the labs are linked to significant tech companies. Republican lawmakers have cautioned against regulatory overreach, inefficiencies in government-managed AI evaluation systems, and the risk of favoritism toward established tech players. They argue that government-managed frameworks could stifle innovation and create barriers for smaller firms and instead advocate for private-sector-led governance and solutions.

In 2025, we will continue to monitor the emergence of AI assurance labs as well as other proposals to minimize algorithmic bias, enhance model robustness, and ensure transparency.

Transforming Supply Chains with AI-Driven Technology

AI and machine learning are rapidly transforming supply chains, driving significant improvements in accuracy, efficiency and resilience. Modern supply chain and manufacturing platforms now integrate real-time insights, generative AI-powered performance reporting, and advanced tools for maintenance and order management. Real-time alerts empower businesses to address performance issues or disruptions in complex supply chains, thereby enhancing operational reliability.

Leveraging untapped data is an emerging trend in biopharma supply chains. To unlock AI’s full potential, companies must transition from fragmented digital initiatives to a cohesive, strategically driven digitalization agenda. Aligning technology investments with supply chain operations aid the achievement of end-to-end transformation, with defined ROI benchmarks and measurable success metrics driving progress and accountability.

Large language models can be used to automatically process large volumes of data, including from certificates of analysis, invoices and other supplier documentation to assess supplier performance and the likelihood of a supply disruption. AI solutions can assist in course correction and restoration of operations after manufacturing or other deviations. AI solutions can also enhance digital twin supply chains, virtual copies of supply chains that can help predict reactions, developments, and other unknown factors by mimicking both physical properties and operational algorithms.

Nonetheless, it is important to note that these novel solutions come with their own set of data-related and cybersecurity concerns. For example, digital twins require layering technology to obtain data and could increase entry points for cybersecurity breaches through the use of Internet of Things (IoT) sensors, which monitor factors such as machine performance resources, and edge computing, which decentralizes data processing centers.