MedTechs Battle with AI for Sustainable Growth and Enhanced Value

MedTechs Battle with AI for Sustainable Growth and Enhanced Value
 
Preface
 
The medical technology industry has experienced significant growth, consistently surpassing the S&P index by ~15 percentage points. This success is rooted in the early 1990s, a time when capital was costly, with interest rates ~10%. However, as we moved closer to 1998, interest rates began to recede, settling just below 7%. This early era of growth was not devoid of challenges. The US was still grappling with the aftermath of the oil embargo imposed in 1973 by the Organization of the Petroleum Exporting Countries (OPEC), which was a response to the American government's support for Israel during the Yom Kippur War and had lasting consequences. The oil crisis triggered hyperinflation, leading to a rapid escalation in the prices of goods and services. In response, the US Federal Reserve (Fed) raised interest rates to a historic high of 17% in 1981, which was aimed at curbing inflation but came at the price of increasing the cost of borrowing. As we entered the 1990s, the landscape shifted. The Fed’s monetary policies began to work, inflation started to decline, and interest rates fell to ~10%, eventually dipping below 7% in 1998. This created conditions for increased investments in research and development (R&D) and the American economy blossomed and benefitted from the internet becoming mainstream. It was during this period that many medical technology companies developed innovative medical devices, which were not only disruptive but also found a receptive global market characterized by significant unmet needs and substantial entry barriers. In the ensuing years, the industry thrived and matured. Fast-forward to the present (2023), and we find ourselves in a different scenario. Over the past five years, numerous large, diversified MedTechs have struggled to deliver value. One explanation for this is that growth of these enterprises over the past three decades, except for the early years, was primarily driven by mergers and acquisitions (M&A), often at the expense of prioritizing R&D. Consequently, many large MedTechs did not leverage evolving technologies to update and renew their offerings and are now heavily reliant on slow-growth markets and aging product portfolios. Navigating a successful path forward would be helped by a comprehensive embrace of artificial intelligence (AI) and machine learning (ML) strategies, since these technologies possess the potential to transform how MedTechs operate, innovate, and serve their stakeholders.
 
In this Commentary

This Commentary explores the role of artificial intelligence (AI) in reshaping the future landscape of the MedTech industry in pursuit of sustainable growth and added value. We focus on the impact AI can have on transforming operational methodologies, fostering innovation, and enhancing stakeholder services. Our aim is to address five key areas: (i) Defining Artificial Intelligence (AI): Describes how AI differs from any other technology in history and sheds light on its relevance within the MedTech sector. (ii) Highlighting AI-Driven MedTech Success: In this section, we preview three leading corporations that have utilized AI to gain access to new revenue streams. (iii) Showcasing a Disruptive AI-Powered Medical Device: Here, we provide an overview of the IDx-DR system, an innovation that has brought disruptive change to the field of ophthalmology. (iv) The Potential Benefits of Full AI Integration for MedTechs: This section briefly describes 10 potential benefits that can be expected from a comprehensive embrace of AI by MedTechs. (v) Potential Obstacles to the Adoption of AI by MedTechs: Finally, we describe some obstacles that help to explain some MedTechs reluctance to embrace AI strategies. Despite the substantial advantages that AI offers, not many large, diversified enterprises have fully integrated these transformative technologies into their operations. Takeaways outline the options facing enterprises.
 
Part 1

Defining Artificial Intelligence (AI)

Artificial Intelligence (AI) is a ground-breaking concept that transcends the simulation of human intelligence. Unlike human cognition, AI operates devoid of consciousness, emotions, and feelings. Thus, it is indifferent to victory or defeat, tirelessly working without rest, sustenance, or encouragement. AI empowers machines to perform tasks once exclusive to human intelligence, including deciphering natural language, recognizing intricate patterns, making complex decisions, and iterating towards self-improvement. AI is significantly different to any technology that precedes it. It is the first instance of a tool with the unique capabilities of autonomous decision making and the generation of novel ideas. While all predecessor technologies augment human capabilities, AI takes power away from individuals.
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AI employs various techniques, including machine learning (ML), neural networks, natural language processing, and robotics, enabling computers to autonomously tackle increasingly complex tasks. ML, a subset of AI, develops algorithms that learn, adapt, and improve through experience, rather than explicit programming. The technology’s versatile applications span image and speech recognition, recommendation systems, and predictive analytics. In the quest to comprehend the intersection of artificial and human intelligence, we encounter Large Language Models (LLMs), like ChatGPT, which recently have gained prominence in corporate contexts. These advanced AI models grasp and generate human-like text by discerning patterns and context from extensive textual datasets. LLMs excel in language translation, content generation, and engaging in human-like conversations, effectively harnessing our linguistic abilities.

Part 2

Highlighting AI-Driven MedTech Success

This section briefly describes three examples of MedTechs that have successfully leveraged AI technologies to illustrate how AI’s growing influence drives improvements in accuracy, efficiency, patient outcomes and in the reduction of costs, which together, and in time, are positioned to transform healthcare.
 
Merative, formally Watson Health, a division of IBM that specialised in applying AI and data analytics to healthcare. In 2022, the company was acquired by Francisco Partners, an American  private equity firm, and rebranded Merative. The company leverages AI, ML, and LLMs to analyse extensive medical datasets that encompass patient records, clinical trials, medical literature, and genomic information. These technologies empower healthcare professionals by facilitating more informed decisions, identifying potential treatment options, and predicting disease outcomes. For instance, Merative employs ML to offer personalised treatment recommendations for cancer patients based on their medical histories and the latest research. Integrating LLMs enables natural language processing to extract insights from medical literature, helping healthcare providers stay current with scientific and medical advancements.
 
Google Health, a subsidiary of Alphabet Inc., focuses on using AI and data analysis to improve healthcare services and patient outcomes. It employs AI and ML to develop predictive models that can identify patterns and trends in medical data, which improve early disease detection and prevention. One notable application is in medical imaging, where the company's algorithms can assist radiologists to identify anomalies in X-rays, MRIs, and other images. LLMs are used to interpret and summarize medical documents, making it easier for healthcare professionals to access relevant information quickly. Google Health also works on projects related to drug discovery and genomics, leveraging ML to analyze molecular structures and predict potential drug candidates.
Medtronic is a global leader in medical technology, specializing in devices and therapies to treat various medical conditions. The company incorporates AI, ML, and LLMs into their devices and systems to enhance patient care. For instance, in the field of cardiology, Medtronic's pacemakers and defibrillators collect data on a patient's heart rhythms, which are then analyzed using AI algorithms to detect irregularities and adjust device settings accordingly. This real-time analysis helps to optimize patient treatment. Medtronic also employs AI in insulin pumps for diabetes management that can learn from a patient's blood sugar patterns and adjust insulin delivery accordingly. Additionally, LLMs are used to extract insights from electronic health records (EHR) and clinical notes, which help healthcare providers to make more personalized treatment decisions.
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Part 3

Showcasing a Disruptive AI-Powered Medical Device

AI has been applied to various medical imaging tasks, including interpreting radiological images like X-rays, CT scans, and MRIs and there are numerous AI-driven medical devices and systems that have emerged and evolved in recent years. As of January 2023, the US Federal Drug Administration (FDA) has approved >520 AI medical algorithms, the majority of which are related to medical imaging. Here we describe just one, the IDx-DR system, which was developed by Digital Diagnostics. In 2018, it became the first FDA-approved AI-based diagnostic system for detecting diabetic retinopathy. If left untreated, the condition can lead to blindness. Globally, the prevalence of the disease among people living with diabetes is ~27% and every year, >0.4m people go blind from the disorder. In 2021, globally there were ~529m people with diabetes, which is expected to double to ~1.31bn by 2050.
 
The IDx-DR device utilizes AI algorithms to analyze retinal images taken with a specialized camera and accurately detects the presence of retinopathy that occurs in individuals with diabetes when high blood sugar levels cause damage to blood vessels in the retina. Significantly, the device produces decisions without the need for retinal images to be interpreted by either radiologists or ophthalmologists, which allows the system to be used outside specialist centres, such as in primary care clinics. Advantages of the system include: (i) Early detection, which can improve outcomes and quality of life for individuals with diabetes. (ii) Efficiency. The system analyzes images quickly and accurately, providing results within minutes, which allows healthcare providers to screen a larger number of patients in a shorter amount of time. (iii) Reduced healthcare costs. By detecting retinopathy at an early stage, the system helps prevent costly interventions, such as surgeries and treatments for advanced stages of the disease, which can lead to significant cost savings for healthcare systems. (iv) Patient convenience. Patients undergo retinal imaging as part of their regular diabetes check-ups, reducing the need for separate appointments with eye specialists, which encourages enhanced compliance.
 
Part 4

The Potential Benefits of Full AI Integration for MedTechs

Large, diversified MedTechs stand to gain significant benefits by fully embracing AI technologies that extend across all aspects of their operations, innovation, and overall value propositions. In this section we briefly describe 10 such advantages, which include enhanced innovation, improved patient outcomes, increased operational efficiency, cost savings, and access to new revenue streams. Companies that harness the full potential of AI will be better positioned to thrive in the highly competitive and rapidly evolving healthcare industry.
 
1. Enhanced innovation and product development
AI technologies have the potential to enhance R&D endeavours. They accomplish this through the ability to dig deep into vast repositories of complex medical data, identifying patterns, and forecasting outcomes. This translates into a shorter timeline for the conception and creation of novel medical technologies, devices, and therapies. In essence, AI quickens the pace of innovation in healthcare. The capabilities of AI-driven simulations and modeling further amplifies its impact. These virtual tools enable comprehensive testing in a digital environment, obviating the need for protracted physical prototyping and iterative cycles, which can shorten the development phase and conserve resources, making the innovation process more cost-effective, and environmentally sustainable.
 
2. Improved patient outcomes
Beyond improving the research landscape, AI improves the quality of patient care by enhancing diagnostic precision through the analysis of medical images, patient data, and clinical histories. Early detection of diseases becomes more precise and reliable, leading to timelier intervention and improved patient outcomes. Additionally, AI facilitates the personalization of treatment recommendations, tailoring them to individual patient profiles and current medical research. This optimizes therapies and increases the chances of successful outcomes and improved patient wellbeing.
 
3. Efficient clinical trials
Increasingly AI algorithms are being used in clinical studies to identify suitable patient cohorts for participation in trials, effectively addressing recruitment challenges and streamlining participant selection. Further, predictive analytics play a role in enhancing the efficiency of trial design. By providing insights into trial protocols and patient outcomes, AI reduces both the time and costs associated with bringing novel medical technologies to market, which speeds up the availability of treatments and facilitates the accessibility of healthcare innovations to a broader population.
 
4. Operational efficiency
Operational efficiency is improved with the integration of AI technologies by refining operations. AI-driven supply chains and inventory management systems play a significant role in optimizing procurement processes. They analyze demand patterns, reduce wastage, and ensure the timely availability of critical supplies. By doing so, companies can maintain uninterrupted operations, enhancing their overall efficiency and responsiveness. Another component of operational efficiency lies in predictive maintenance, which can be improved by AI. Through continuous monitoring and data analysis, AI can predict equipment failures before they occur. Such a proactive approach minimizes downtime and ensures manufacturing facilities remain compliant and in optimal working condition. Consequently, healthcare providers experience improved operational efficiency, strengthened compliance, and a reduction in costly disruptions. The automation of routine tasks and processes via AI relieves healthcare professionals from repetitive duties and frees up resources that can be redirected towards more strategic and patient-centric initiatives. This reallocation reduces operational costs while enhancing the quality of care provided.
 
5. Cost savings
Beyond automation, AI-driven insights further uncover cost efficiencies within healthcare organizations. AI identifies areas where resource allocation and utilization can be optimized, which can result in cost reduction strategies that are both data-informed and effective. AI's potential extends to the generation of innovative revenue streams. Corporations can develop data-driven solutions and services that transcend traditional medical devices. For instance, offering AI-driven diagnostic services or remote patient monitoring solutions provides access to new revenue streams. Such services improve patient care and contribute to the financial sustainability of enterprises. Further, AI-enabled healthcare services lend themselves to subscription-based models, ensuring consistent and reliable revenue over time. Companies can offer subscription services that provide access to AI-powered diagnostics, personalized treatment recommendations, or remote monitoring, which have the capacity to diversify revenue streams and enhance longer-term financial stability.
 
6. New revenue streams
AI's ability to analyze vast datasets positions MedTechs to unravel the interplay of genetic, environmental, and lifestyle factors that shape individual health profiles. With such knowledge, personalized treatment plans and interventions can be developed, ensuring that medical care is tailored to each patient's unique needs and characteristics. This level of customization optimizes outcomes and minimizes potential side effects and complications. AI's ability to process vast amounts of patient data and detect patterns, anomalies, and correlations, equips healthcare professionals with the knowledge needed to make more informed decisions. Such insights extend beyond individual care, serving as the basis for effective population health management and proactive disease prevention strategies. In short, AI transforms data into actionable intelligence, creating a basis for more proactive and efficient healthcare practices.
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7. Regulatory compliance and safety
In an era of stringent healthcare regulations, AI is a reliable ally to ensure compliance and enhance safety standards. Through automation, AI streamlines documentation, data tracking, and quality control processes, reducing the risk of errors and oversights. Also, AI-powered systems excel in the early detection of anomalies and potential safety issues, which increase patient safety and the overall quality of healthcare solutions and services. This safeguards patient wellbeing and protects the reputation and credibility of companies.
8. Competitive advantage
MedTechs that are early adopters of AI stand to gain a distinct competitive advantage. They can offer AI-powered solutions and services that deliver superior clinical outcomes and improve overall patient experience. By harnessing the potential of AI, companies can position themselves as leaders in innovation and technological capabilities, likely drawing a loyal customer base, valuable partnerships, collaborations, and investments.
 
9. Talent attraction and retention
Embracing AI technologies also has an impact on talent attraction and retention. The allure of working on novel AI projects that improve lives attracts scarce tech-savvy professionals who seek to be part of dynamic, purposeful, and forward-thinking teams. Such talent contributes to a skilled workforce capable of extending the boundaries of AI innovation within MedTech companies. Further, fostering a culture of innovation through AI adoption encourages employee engagement and job satisfaction, leading to improved talent retention.
 
10. Long-term sustainability
The integration of AI goes beyond immediate advantages; it positions MedTechs for longer-term strategic growth and resilience. As the healthcare landscape continues to evolve, adaptability and innovation become more important. AI enables companies to adapt to changing market dynamics, navigate regulatory challenges, and remain relevant amidst industry transformations. By staying at the forefront of technological advancements, companies ensure their relevance and contribute to shaping the future healthcare landscape.
 
Part 5

Potential Obstacles to the Adoption of AI by MedTechs

The integration of AI technologies into numerous industries has demonstrated its potential to significantly enhance operations, improve R&D, and create new revenue streams. However, despite AI’s potential to contribute significant benefits for business enterprises, its adoption by many large, diversified medical technology companies has been limited and slow. This section describes some factors that help to explain the reluctance of senior MedTech executives to fully embrace AI technologies, which include an interplay of organizational, technical, and industry-specific issues. Without overcoming these obstacles, MedTechs risk losing the growth and value creation they once experienced in an earlier era.

Demographics of senior leadership teams
According to Korn Ferry, an international consultancy and search firm, the average age for a C-suite member is 56 and their average tenure is 4.9 years, although the numbers vary depending on the industry. The average age of a CEO across all industries is 59. If we assume that the MedTech industry mirrors this demographic, it seems reasonable to suggest that many corporations have executives approaching retirement who may be more risk averse and oppose the comprehensive introduction of AI technologies due to a fear of losing benefits they stand to receive upon retirement.

Organizational inertia and risk aversion
Large medical technology companies often have well-established structures, processes, and cultures that resist rapid change. In such an environment, executives might be hesitant to introduce AI technologies due to concerns about disrupting existing workflows, employee resistance to learning new skills, and the fear of failure. The risk-averse nature of the medical technology industry, where patient safety is critical, further amplifies executives' cautious approach to implementing unproven AI solutions.
 
Technical challenges and skill gaps
AI implementation requires technical expertise and resources. Many MedTech executives might lack a deep understanding of AI's technical capabilities, making it difficult for them to evaluate potential applications. Further, attracting and retaining AI talent is highly competitive, and the scarcity of professionals skilled in both medical technology and AI can hinder successful implementation.
Regulatory and ethical concerns
The medical field is heavily regulated to ensure patient safety and data privacy. Incorporating AI technologies introduces additional layers of complexity in terms of regulatory compliance and ethical considerations. Executives might hesitate to navigate these legal frameworks, fearing potential liabilities and negative consequences if AI systems are not properly controlled or if they lead to adverse patient outcomes.
Long development cycles and uncertain ROI
The R&D cycle in the medical technology industry is prolonged due to rigorous testing, clinical trials, and regulatory approvals. Although AI technologies have the capabilities to enhance R&D efficiency, they can introduce additional uncertainty and complexity, potentially extending development timelines. Executives could be apprehensive about the time and resources required to integrate AI into their R&D processes, especially if the return on investment (ROI) remains uncertain or delayed.
 
Industry-specific challenges
The medical technology industry has unique challenges compared to other sectors. Patient data privacy concerns, interoperability issues, and the need for rigorous clinical validation can pose barriers to AI adoption. Executives might view these complexities as additional hurdles that could hinder the successful implementation and deployment of AI solutions.
  
Existing Revenue Streams and Incremental Innovation
Many large, diversified MedTechs generate substantial revenue from their existing products and services. Executives might be reluctant to divert resources towards AI-based ventures, fearing that these investments could jeopardize their core revenue streams. Additionally, a culture of incremental innovation prevalent in the industry might discourage radical technological shifts like those associated with AI.
 
Takeaways
 
Hesitation among MedTechs to integrate AI technologies poses the threat of missed opportunities, diminished competitiveness, and sluggish growth. This reluctance hinders innovation and limits the potential for enhanced patient care. Embracing AI is not an option but a strategic imperative. Failure to do so means missing opportunities to address unmet medical needs, explore new markets, and access new revenue streams. The potential for efficiency gains, streamlined operations, and cost reductions across R&D, manufacturing and supply chains is significant. Companies fully embracing AI gain a competitive advantage, delivering innovative solutions and services that improve patient outcomes and cut healthcare costs. Conversely, those resisting AI risk losing market share to more agile rivals. AI’s impact on analysing vast amounts of complex medical data, accelerating discovery, and enhancing diagnostics is well established. MedTechs slow to leverage AI may endure prolonged R&D cycles, fewer breakthroughs, and suboptimal resource allocation, jeopardising competitiveness and branding them as ‘outdated’. In today’s environment, attracting top talent relies on being perceived as innovative, a quality lacking in AI-resistant MedTechs. As AI disrupts industries, start-ups and smaller agile players can overtake established corporations failing to adapt. A delayed embrace of AI impedes progress in patient care, diagnosis, treatment, and outcomes, preventing companies from realising their full potential in shaping healthcare. The time to embrace AI is now to avoid irreversible setbacks in a rapidly evolving MedTech ecosystem.

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