Healthcare

<p style="text-align: justify;"><span data-preserver-spaces="true">In recent years, the integration of Artificial Intelligence (AI) has transformed the landscape for medical device companies, offering new possibilities for growth, innovation, and collaboration. However, in the face of challenges such as unsustainable healthcare costs and increased competition, companies must not only embrace AI but also adopt a strategic approach to collaboration, ecosystem establishment, and organizational flexibility. This article explores the multifaceted role of AI in the growth of medical device companies amidst these challenges and outlines a strategic vision for success in the future.</span></p><p style="text-align: justify;">&nbsp;</p><h2 style="text-align: justify;"><span style="font-size: 14pt;"><strong>Innovation and Product Development</strong></span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">AI plays a pivotal role in fostering innovation within medical device companies. Through machine learning algorithms and predictive analytics, these companies can streamline and expedite the product development process. AI assists in identifying patterns, predicting potential issues, and optimizing designs, ultimately leading to the creation of more advanced and effective medical devices.<br /><br /></span></p><h2 style="text-align: justify;"><span style="font-size: 14pt;"><strong>Personalized Medicine</strong></span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">The era of one-size-fits-all medical treatments is evolving into a more personalized approach, and AI is at the forefront of this transformation. Medical device companies leverage AI to analyze vast datasets, including genetic information, patient history, and real-time health monitoring. This enables the development of medical devices tailored to individual patient needs, resulting in more effective and targeted treatments.<br /><br /></span></p><h2 style="text-align: justify;"><span style="font-size: 14pt;"><strong>Remote Patient Monitoring</strong></span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">AI-powered devices enable real-time remote patient monitoring, allowing healthcare providers to gather crucial data without the need for frequent in-person visits. This not only improves patient comfort but also facilitates early detection of potential health issues. Medical device companies are increasingly investing in AI-driven technologies for applications such as continuous glucose monitoring, cardiac monitoring, and sleep tracking.<br /><br /></span></p><h2 style="text-align: justify;"><span style="font-size: 14pt;"><strong>Enhanced Diagnostics and Imaging</strong></span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">AI significantly enhances diagnostic capabilities in the medical device industry. Image recognition algorithms can analyze medical images, such as X-rays and MRIs, with incredible accuracy. This leads to quicker and more precise diagnoses, reducing the margin of error and improving patient outcomes. Medical device companies are incorporating AI into diagnostic devices, making them powerful tools for healthcare professionals.<br /><br /></span></p><h2 style="text-align: justify;"><span style="font-size: 14pt;"><strong>Operational Efficiency</strong></span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">AI-driven solutions contribute to increased operational efficiency within medical device companies. Through automation of routine tasks, predictive maintenance, and supply chain optimization, AI helps these companies reduce costs and streamline their operations. This efficiency translates into faster time-to-market for new products and improved overall competitiveness.<br /><br /></span></p><h2 style="text-align: justify;"><span style="font-size: 14pt;"><strong>Strategic Vision for the Future</strong></span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">The days of simply manufacturing a device and selling it to healthcare providers via distributors have long vanished. Medical device manufacturers must reinvent their traditional business and operating models to adapt to the future. This involves integrating intelligence into their portfolios, delivering services beyond the device, and investing in enabling technology. A three-pronged strategy is proposed by <a href="https://kpmg.com/us/en/articles/2023/medical-devices-2030.html" target="_blank" rel="noopener">KPMG</a>:</span></p><ul style="text-align: justify;"><li><strong><span data-preserver-spaces="true">Integrating Intelligence</span></strong></li><ul><li class="ql-indent-1"><span data-preserver-spaces="true">Positive influence on the care journey</span></li><li class="ql-indent-1"><span data-preserver-spaces="true">Connection with customers, patients, and consumers</span></li></ul><li><strong><span data-preserver-spaces="true">Delivering Services Beyond the Device</span></strong></li><ul><li class="ql-indent-1"><span data-preserver-spaces="true">Shifting from cost to smart value</span></li><li class="ql-indent-1"><span data-preserver-spaces="true">Intelligence beyond services</span></li></ul><li><strong><span data-preserver-spaces="true">Investing in Enabling Technology</span></strong></li><ul><li class="ql-indent-1"><span data-preserver-spaces="true">Making choices to support parallel business models</span></li><li class="ql-indent-1"><span data-preserver-spaces="true">Tailored strategies for customers, patients, and consumers</span></li></ul></ul><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;"><span data-preserver-spaces="true">Additionally, an 'outside-in' perspective is crucial for preparing for the future. Medical device companies must consider dynamic external forces from new entrants, evolving technologies, and emerging markets.<br /><br /></span></p><h2 style="text-align: justify;"><span style="font-size: 14pt;"><strong>Collaboration and Ecosystem Establishment</strong></span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">Executing business and operating model choices will likely require capabilities from an expanded external network. While M&amp;A activity intended to build scale and diversify the portfolio will continue, the shift to services and intelligence should generate deal activity focused on establishing corresponding capabilities, both within and outside the value chain. Companies will need to institute a systemic process to identify strategic alliance partners and an internal capability to effectively manage their ecosystem.</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">Collaborating widely, including cross-sector partnerships, conducting joint experiments, and even considering competition, will be essential to meet the goals for the chosen configurations. Establishing a collaborative ecosystem enables medical device companies to leverage a diverse set of skills, resources, and expertise, fostering innovation and addressing the complexities of the evolving healthcare landscape.<br /><br /></span></p><h2 style="text-align: justify;"><span style="font-size: 14pt;"><strong>Adopt a Flexible, Modular Organizational Structure</strong></span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">In a dynamic environment, medical device companies will need to react quickly to market opportunities and move at 'deal speed' to realize value from growth transactions. To achieve this, adopting a flexible, modular organizational structure is essential. While large multi-billion dollar corporations may not operate as start-ups, active steps towards a more agile and nimble organizational structure are crucial. Processes should be streamlined, and people empowered, ensuring adequate levels of governance by segment while allowing for faster decision-making, especially as it relates to the portfolio (products, services, and intelligence) and technology.</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">Generative AI, a subset of artificial intelligence, may significantly impact medical device company operating models and shared services. Generative AI can assist in creating novel designs, prototypes, and solutions, fostering a more agile approach to innovation. It enables rapid prototyping and experimentation, aligning with the need for quick reactions to market opportunities. Moreover, Generative AI can enhance decision-making processes by simulating potential outcomes and providing insights, contributing to more informed and agile decision-making within the organization.<br /><br /></span></p><h2 style="text-align: justify;"><span style="font-size: 14pt;"><strong>Conclusion</strong></span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">In the face of challenges such as unsustainable healthcare costs and increased competition, medical device companies must adapt and evolve to stay relevant. The <a href="https://www.mckinsey.com/industries/healthcare/our-insights/tackling-healthcares-biggest-burdens-with-generative-ai#/" target="_blank" rel="noopener">role of AI in this transformation</a> is undeniably transformative, offering a pathway for companies to offer value beyond the device and address healthcare's broader challenges. As technology continues to advance, those medical device companies that embrace and leverage AI are poised for sustained growth, innovation, and positive contributions to the healthcare landscape. The strategic vision for 2030, coupled with collaborative efforts, ecosystem establishment, and agile organizational structures, provides a comprehensive approach to success in a dynamic and evolving healthcare environment.</span></p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;"><em><span data-preserver-spaces="true">This article was contributed by our expert <a href="https://www.linkedin.com/in/sridharanjanappa/" target="_blank" rel="noopener">Sridhar Anjanappa</a></span></em></p><p style="text-align: justify;">&nbsp;</p><p style="text-align: justify;">&nbsp;</p><h3 style="text-align: justify;"><span style="font-size: 18pt;"><strong>Frequently Asked Questions Answered by&nbsp;Sridhar Anjanappa</strong></span></h3><p style="text-align: justify;">&nbsp;</p><h2 style="text-align: justify;"><span style="font-size: 12pt;" data-preserver-spaces="true">1. How can medical device companies ensure the accuracy and performance of AI algorithms embedded in medical devices? What validation processes are in place to confirm the efficacy of AI-driven functionalities?</span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">Medical device companies have to ensure the quality and appropriateness of the data used to train, test, and validate the AI medical device system. Data quality can depend on many factors, including incorrect data, incomplete data, subjective data, inconsistent data, incorrect handling of outliers in data, and data representing the right population on which the AI-based medical device will be used. They should also consider if the algorithm is configurable and validate if the configuration data source is impacting the performance.&nbsp;&nbsp;</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">The accuracy and performance of the AI system ensure that the AI model effectively manages the data. For example, respiratory rate data from a pulse oximeter might not be sufficient to determine if the patient is experiencing a stroke, which can lead to underfitting. Alternatively, a system interprets noise in the data as being a true signal and reacts accordingly causing overfitting.</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">Data is the lifeblood of medical device companies and has a significant impact on their AI-based medical devices. That&rsquo;s why it&rsquo;s pivotal for companies to have the right set of data quality tools to determine the health of the data they are relying on. There are several tools available to manage data quality for AI models such as TruEra which provides a suite of tools to manage the quality of AI models throughout their lifecycle, it offers features such as model performance analysis, societal impact analysis, operational compatibility analysis, and data quality analysis.&nbsp;</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">The European &ldquo;ITFoC (Information Technology for the Future Of Cancer)&rdquo; consortium proposed a framework for validating AI in precision medicine, which specifies seven key steps:</span></p><ul style="text-align: justify;"><li><span data-preserver-spaces="true">The intended use of AI</span></li><li><span data-preserver-spaces="true">The target population</span></li><li><span data-preserver-spaces="true">The timing of AI evaluation</span></li><li><span data-preserver-spaces="true">The datasets used for evaluation</span></li><li><span data-preserver-spaces="true">The procedures used for ensuring data safety (including data quality, privacy, and security)</span></li><li><span data-preserver-spaces="true">The metrics used for measuring performance</span></li><li><span data-preserver-spaces="true">The procedures used to ensure that the AI is explainable<br /><br /></span></li></ul><h2 style="text-align: justify;"><span style="font-size: 12pt;" data-preserver-spaces="true">2. How do the companies ensure regulatory compliance for AI-powered medical devices?</span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">Medical device companies can ensure regulatory compliance for AI-powered medical devices by following the guidelines set forth by regulators such as the FDA and MDR. These guidelines require AI-driven medical devices to comply with state-of-the-art requirements and provide objective evidence for repeatability and reliability.</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">The FDA has proposed a new regulatory framework for AI/ML devices following a total product lifecycle (TPLC) approach. The framework for AI-based devices centers on four pillars: Good machine learning practices (GMLP), premarket review for safety and effectiveness, established pre-specifications and algorithm change protocol, and patient-focused transparency and real-world performance monitoring. The Association of Notified Bodies for Medical Devices in Germany (IG-NB) has issued a comprehensive &ldquo;Requirements Checklist&rdquo; for assessing the safety of AI-enabled medical technologies.</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">AAMI 34971 released TIR in 2023 which guides the application of ISO 14971 to machine learning in artificial intelligence. The standard can help manufacturers identify the particular hazards that can be introduced by AI and as a result make their devices safer, more effective, and more efficient. Its use can also help device manufacturers achieve compliance more readily, and instill greater confidence in their products, which in turn assists new market entry, accelerates product innovation, develops expertise with AI safety in medical devices, improves operational efficiency, and strengthens overall risk management.</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">Medical device manufacturers should also pay attention to other standards that are relevant for medical devices with machine learning, including ISO 13485:2016, IEC 62304, IEC 62366-1, and IEC 823042.</span></p><p style="text-align: justify;">&nbsp;</p><h2 style="text-align: justify;"><span style="font-size: 12pt;" data-preserver-spaces="true">3. Is there room for customization of AI algorithms based on specific user requirements or healthcare environments?</span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">There is significant room for customization of AI algorithms based on specific user requirements and healthcare environments. The healthcare sector is diverse, with various specialties, unique patient populations, and specific workflow demands.&nbsp;</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">Different healthcare settings, such as hospitals, clinics, and home care, have unique requirements. Customizing AI algorithms allows for the development of solutions that can adapt to the specific needs of each environment. Customizing AI algorithms enables the development of specialized tools for areas such as radiology, pathology, cardiology, and oncology, ensuring that the algorithms are tailored to the nuances of each specialty. Patients have diverse characteristics, medical histories, and responses to treatments. Customization allows AI algorithms to take into account individual patient variability, leading to more personalized and effective healthcare interventions.</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">Healthcare professionals have unique preferences and workflows. Customizing AI tools to align with these preferences and workflows enhances user acceptance and adoption, as the technology becomes more seamlessly integrated into daily practices.</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">In summary, the customization of AI algorithms in healthcare is not only beneficial but also essential for addressing the diverse and evolving nature of the industry. Tailoring AI solutions to specific user requirements, healthcare settings, and regulatory frameworks enhances their relevance, effectiveness, and acceptance in improving patient outcomes and healthcare delivery.<br /><br /></span></p><h2 style="text-align: justify;"><span style="font-size: 12pt;" data-preserver-spaces="true">4. How do collaborative efforts in AI impact the overall market dynamics of the medical device industry?</span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">The future of AI in healthcare lies in its ability to provide tailored solutions that address the unique requirements of patients, healthcare providers, and the overall healthcare ecosystem. The development and customization of AI algorithms should be a collaborative effort involving healthcare professionals, technologists, and policymakers to ensure the successful integration and adoption of AI in improving patient outcomes and healthcare delivery. Partnerships enable the creation of advanced medical devices that leverage AI for improved diagnostics, monitoring, and treatment.&nbsp;</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">The cross-industry integration results in the development of interconnect systems, where AI-enabled medical devices seamlessly communicate with each other. For example, IBM Watson Health collaborated with Medtronic to develop a diabetes management solution. This AI-powered system combines data from Medtronic's continuous glucose monitors with Watson's analytics capabilities to provide personalized insights for diabetes patients. Also shared resources and knowledge in collaborative AI efforts can lead to cost efficiencies, as seen in joint ventures like GE Healthcare and NVIDIA working together to develop AI-powered imaging solutions, potentially reducing development costs through shared expertise and infrastructure.</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">Collaborative efforts in AI within the medical device industry drive innovation, accelerate product development, and enhance interoperability, leading to a more dynamic and rapidly evolving market landscape. These collaborations not only optimize costs and facilitate regulatory compliance but also foster global market expansion, prioritize user-centric solutions, and address ethical considerations, collectively shaping the industry's growth and competitiveness.</span></p><h2 style="text-align: justify;"><span style="font-size: 12pt;" data-preserver-spaces="true"><br />5. Can you provide examples of how medical device companies can learn and adapt based on experiences with generative AI and the flexible organizational structure?</span></h2><p style="text-align: justify;"><span data-preserver-spaces="true">Generative AI, a cutting-edge technology, offers numerous benefits across the entire medtech industry, enhancing efficiency and transforming various aspects of the value chain. One significant application is in Research and Development (R&amp;D) and software development. Take Github Copilot X, for instance &ndash; it's a generative model trained on vast amounts of code that translates natural language commands into code suggestions. This not only saves time for developers but also improves the quality of the code.</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">Moving beyond software, generative AI contributes to creative innovation, especially in designing new products. Companies can leverage generative design tools from firms like PTC or Autodesk to explore novel and unique product designs that may not have been possible through traditional methods.</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">Moreover, generative AI simplifies complex tasks in areas such as regulatory compliance. Converting internal documents into regulatory submissions or clinical trial reports, often tedious and lengthy processes can be streamlined for greater efficiency. Although there are startups actively working on solutions for regulatory challenges, none have reached commercial availability just yet.</span></p><p style="text-align: justify;"><span data-preserver-spaces="true">In operations, it can improve efficiency by automating tasks and predicting potential issues in manufacturing processes. Additionally, generative AI facilitates personalized customer interactions, contributing to better post-sales customer support. Its ability to analyze vast datasets also aids in making data-driven decisions, ultimately fostering a more agile and responsive medical device organization that can navigate challenges and seize opportunities in the rapidly evolving healthcare industry.</span></p><p>&nbsp;</p><p>&nbsp;</p>