Page 8 - ITPS-8-3

INNOSC Theranostics and
Pharmacological Sciences AI in medical device safety

The primary goal of materiovigilance is to safeguard incorporation of AI into materiovigilance systems can
public health by monitoring and addressing potential address current challenges in medical device monitoring
safety issues related to medical equipment. It plays a vital and ultimately enhance patient outcomes.
3
role in improving medical device efficiency and design,
reducing complications from devices, and alerting patients 2. Challenges in the traditional
and health-care providers to counterfeit or substandard materiovigilance system
devices. As a crucial component of health policy in both Despite the implementation of materiovigilance programs
4
public and private health-care settings, materiovigilance in numerous countries, post-marketing surveillance
helps minimize the likelihood of incidents caused by of medical devices remains less advanced and reliable
medical equipment. With the global increase in medical compared to that of medicines. This circumstance
5
1,7
device use, materiovigilance has become increasingly suggests that traditional methods may be inadequate in
important for ensuring patient safety and promoting the monitoring and managing risks associated with medical
responsible use of life-saving devices. 6 devices once they are on the market.
Different countries and regions, such as the United There are significant variations in the materiovigilance
States, the European Union, Japan, China, and India, have regulatory systems of different nations, and there is
distinct systems for implementing their materiovigilance insufficient empirical evidence to establish the overall
programs. For instance, the Materiovigilance Program of superiority of any one system. This lack of standardization
3
3
India, established on July 6, 2015, aims to generate safety can lead to inconsistencies in how adverse events are
data and track adverse events related to medical devices. reported and addressed globally.
1,2
Interestingly, although many nations have established
materiovigilance initiatives, these programs are often less One of the primary issues that the world encounters is
developed and refined compared to the systems in place the underreporting of adverse events. Healthcare workers
for medications. This limitation emphasizes the ongoing often struggle to translate their knowledge and positive
7
need for efforts to improve post-market surveillance of attitudes into effective reporting of medical device adverse
2
medical devices. As health-care technology advances, events. This situation indicates that conventional systems
robust materiovigilance procedures are becoming may not be adequately encouraging reporting from those
increasingly crucial, particularly with the integration of most likely to encounter these events.
artificial intelligence (AI). AI has revolutionized post- Challenges including the absence of global standards
market surveillance by enabling more effective signal and poor reporting protocols underscore the need
detection, risk assessment, and regulatory compliance. for continuous strengthening and enhancement of
8
AI is transforming health-care monitoring by providing materiovigilance programs to improve patient safety and
previously unattainable capabilities in patient care, disease medical device monitoring.
detection, and health management. Machine learning Over the past decade, AI has revolutionized
(ML) algorithms and sophisticated data analysis enable materiovigilance by automating adverse event detection,
the processing of large volumes of medical data, including data analysis, and pattern recognition. Traditional
electronic health records, medical imaging, and real-time materiovigilance relied on manual data entry, static databases,
patient data from medical devices. 9,10
and reactive approaches, often resulting in delayed detection
However, the use of AI in health-care monitoring of safety signals. In contrast, modern AI-driven systems
raises concerns related to interpretability, algorithm leverage real-time monitoring, natural language processing
bias, and data privacy, emphasizing the need for (NLP), and predictive analytics to proactively identify risks
transparent and ethically sound AI implementation within from vast datasets, including unstructured sources such as
8
materiovigilance frameworks. In addition, the emergence social media and medical records. AI enhances accuracy,
of AI/ML-enabled medical devices presents new regulatory reduces reporting biases, and facilitates faster regulatory
challenges, making it essential to incorporate sustainability compliance. However, issues such as data privacy and
principles into the materiovigilance ecosystem. 11 algorithm transparency remain pivotal in ensuring the

Despite these challenges, AI holds immense potential efficacy and reliability of AI in materiovigilance. 8,9
for health-care monitoring. By integrating big data Timely reporting of any event occurrence is
analytics, ML, and blockchain technology, AI can transform important, and the reporting period is outlined in Table 1.
patient care models, streamline health-care delivery, and Table 2 discusses the differences in medical device
ultimately improve patient outcomes while reducing vigilance programs in India, the United States, and the
health-care costs. 12,13 The review aims to explore how the United Kingdom.

Volume 8 Issue 3 (2025) 2 doi: 10.36922/itps.6204