AI and its rapid evolution in the medical device industry

Artificial Intelligence (AI) is reforming the healthcare industry, particularly the medical devices industry. AI in medical devices is driving unprecedented opportunities and advancements in areas such as diagnostics, treatment, patient care and data analytics, pushing the boundaries of medical innovation. From clinical uses to administrative support, in clinical and primary care settings, medical devices are used to inform and support healthcare and pharmaceutical professionals. These advancements have positively impacted patients by improving diagnostic accuracy, enhancing treatment effectiveness, and providing more personalised care. In this blog, we will explore the current uses, challenges and future opportunities of AI in medical devices.

What is AI and machine learning in life sciences?

In the medical devices industry, AI is positioning itself as a powerful new tool to help researchers discover groundbreaking treatments. A popular application of AI that is widely used is machine learning. It is a subset of artificial intelligence that helps machines to improve their performance of tasks based on large amounts of data. Machine learning systems can identify patterns, help make decisions and improve their performance with little to no human intervention.

By leveraging machine learning, the medical device industry could help to transform the delivery of healthcare and treatments by improving efficiencies and outcomes. Additionally, AI and machine learning are already used to interpret large amounts of pre-existing data, allowing researchers make informed and more accurate decisions.

Key Areas That AI Is Advancing Medical Devices

The main areas where AI is driving innovation in medical devices are:

• Treatment discovery, development and safety

• Automation of patient results

• Manufacturing and supply chain management

• Clinical trial selection and recruitment retention

• Assistive technology development

What are the applications of AI in healthcare?

AI is revolutionising medical devices by enhancing diagnostics for earlier and more accurate disease detection, personalising treatment plans to each patient's unique needs and enabling predictive analytics to foresee potential health issues, ultimately improving patient care and outcomes. It improves diagnostic accuracy and speed through advanced imaging analysis, creates customised treatments based on patient data and predicts patient outcomes to enable proactive care, ultimately leading to better patient outcomes, more effective treatments. For example, leveraging AI in healthcare, this can reduce patient wait times by up to 30%.

Additionally, AI automates administrative tasks, boosting operational efficiency and supports continuous patient monitoring with real-time data for better management.

Personalisation

One of the most impactful ways that AI is being used in healthcare is in its capacity to personalise treatments for patients through medical devices. This is because AI can analyse data in real time as medical devices are being used by clinicians. The medical device can then apply the treatment outcomes, guided by the real-time data recorded by AI, to provide a personalised service to patients.

PeraHealth, an American firm, developed the Rothman Index  (introduced in 2011), a comprehensive scoring system for assessing patient health. This system utilises data from electronic health records, patient vital signs and laboratory results to generate scores. It offers clinicians a visual dashboard, helping them to monitor a patient's condition in real-time. Leveraging machine learning, doctors can discover whether a patient requires their intervention. This ensures that healthcare professionals can identify patients at risk and detect changes before they become critical. By using AI to tailor treatments for cancer patients, it reduces the risk of many going to intensive care units.

Automation

The use of AI in medical devices has resulted in increased amounts of streamlined automation. It can reduce the risk of human error in complex procedures. For example, AI algorithms are used to help map brain tumour surgeries. In addition to this, AI has the potential to spot opportunities that may be missed by humans, by combining a large amount of real-time data and patient records.

In the UK, the Chelsea and Westminster Foundation Trust utilised Ufonia’s Dora (introduced in 2024), an AI clinical assistant, to revolutionise their cataract care pathway by reducing waiting times and limited capacity. Dora conducts telephone-based voice conversations with patients at various stages of their cataract journey. This includes pre-operative health screenings, surgery and appointment reminders, post-operative checks and patient-reported outcome measures. The technology employs AI natural language processing to interpret patients' responses. It aims to make AI accessible, so patients do not need any technological knowledge, user accounts, hardware devices or training — they simply engage in a conversation.

Enhancement

Applications of AI in the healthcare and life sciences industries are being used to improve medical devices to elevate both health professional and patient experiences. By employing established methods to analyse large datasets, machine learning systems can discern patterns, aid in decision-making and enhance their performance with minimal human involvement. Plus, AI can observe maintenance needs, improving device reliability, making sure that patient support remains consistent.

Top 10 pharma company, Johnson & Johnson, is developing digital solutions for the operating theatre that utilise AI algorithms to swiftly create a "highlight reel" of surgical videos, according to Shan Jegatheeswaran, Global Vice President of MedTech Digital at Johnson & Johnson. This innovation allows surgeons to review key moments from their procedures in just minutes. Without the aid of

Cost Reduction

One of the biggest advantages of using AI in medical devices is cost reduction. Because some tasks can be conducted by AI instead of humans, it reduces labour costs for clinicians and researchers. An area that has seen significant cost reductions is through diagnostics. Advanced algorithms and machine learning models accelerate more accurate diagnoses of patients, reducing the need for expensive and extensive testing in some cases. Plus, any early detections can reduce the need for invasive treatments and the cost to society who are funding healthcare systems.

The team at Cedars-Sinai Medical Center in Los Angeles has acknowledged the transformative potential of AI in healthcare delivery. They are using it to mine common chest CT scans to predict mortality, reducing costs. By improving both efficiency and accuracy, it reduces the need for repeat scans and minimises misdiagnosis risks.

Challenges for AI in Medical Devices

Although the use of AI in the medical devices industry is welcomed by many and offers great potential, it still has hurdles to overcome. Some examples of the challenges that AI brings to the medical devices industry are:

• Ethical implications of drawing data from multiple sources

• Data biases that AI can’t distinguish between

• Ensuring that all data sets produce high-quality and not just high-quantity data

• Lack of global code of conduct for AI

• Overlooking patient safety as AI directly negatively impacts patients

Opportunity for AI in Medical Devices

The use of AI in medical devices has already attracted a lot of attention and will bring with it many opportunities for life science companies to harness. AI has the potential to facilitate the development of innovative devices and applications, opening new markets and driving competitive advantages. Overall, AI's capabilities can significantly elevate the efficiency and effectiveness of medical technologies. Medical device companies could benefit from the opportunities that AI will bring, including:

• Improved data analysis of large datasets

• Continual clinical evaluation of medical devices

• Incorporation of possibilities that would have been missed by humans

• Reduction in cost and time for clinical trials and other medical operations

• Personalisation of treatment

• Enhancement of operational efficiency

• Implementation of remote monitoring

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