Transforming Cutting-Edge Healthcare has emerged as one of the major success stories of modern times. Advances in medical science have accelerated rapidly, increasing life expectancy across the globe. However, as people live longer, healthcare systems face rising demand, escalating costs, and a workforce under pressure to meet patient needs.

This demand is driven by several powerful factors: an aging population, evolving patient expectations, lifestyle changes, and ongoing medical innovation, among others. Of these, the effects of an aging population are particularly significant. By 2050, one in four individuals in Europe and North America will be over 65—placing additional strain on health systems to care for patients with more complex medical needs. Managing these patients is costly and requires a shift from episodic care to a more proactive approach centered on long-term care management.

Healthcare spending is not keeping pace with these demands. Without fundamental structural and transformational reforms, health systems risk becoming unsustainable. A larger workforce is also essential; although the global economy could generate 40 million new healthcare jobs by 2030, there will still be a projected shortage of 9.9 million physicians, nurses, and midwives worldwide during the same period, according to the World Health Organization. It is critical not only to attract, train, and retain more healthcare professionals but also to ensure they spend their time on activities that deliver the greatest value—direct patient care.

AI, building on automation, has the potential to transform healthcare and help address some of these challenges. AI can be broadly understood, as defined by the European Parliament, as “the ability of a computer program to perform tasks or reasoning processes typically associated with human intelligence.” By supporting better care outcomes and enhancing efficiency and productivity, AI can improve the daily work of healthcare professionals, allowing them to dedicate more time to patients, boosting morale, and aiding staff retention. AI can also accelerate the development of life-saving treatments. At the same time, it raises questions regarding its effects on patients, healthcare workers, and systems, as well as ethical considerations about the use of AI and the data behind it.

This report by EIT Health and McKinsey & Company seeks to inform the discussion around AI in healthcare, particularly concerning its impact on practitioners and organizations. It highlights key priorities and trade-offs across various parts of the healthcare system in Europe and beyond. The report draws on proprietary research and analyses conducted by EIT Health and McKinsey & Company, including work from the McKinsey Global Institute on the future of work in the age of automation and AI, an assessment of AI’s impact on healthcare practitioners in Europe, 62 in-depth interviews with healthcare and AI leaders, and an online survey of 175 healthcare professionals, investors, and AI startup executives. Given the rapid pace of AI innovation in healthcare, the report offers a unique perspective from the frontline of care delivery and innovation, reflecting the latest stakeholder views on AI’s potential, current adoption, and barriers to progress.

Finally, to illustrate AI’s existing contributions, the report examines specific examples of AI solutions in six key areas with direct patient impact, as well as three areas of the healthcare value chain where AI could be further expanded.

In doing so, the report makes a distinct contribution to the discussion on AI’s impact in healthcare in four key ways: 1) capturing decision makers’ perspectives on this rapidly evolving field, where developments from just a year ago are now considered “outdated”; 2) introducing a rigorous new approach to assess how automation and AI affect specific skills and tasks in European healthcare; 3) providing an extensive review of use cases that demonstrate the potential AI is already beginning to realize; and 4) offering a frontline perspective by gathering insights from healthcare professionals, investors, and startup leaders on the true potential, opportunities, and challenges.

The report does not aim to cover every aspect of this complex issue, particularly regarding AI ethics or risk management, but it does reflect initiatives on this topic driven by EIT Health and other EU bodies. Similarly, while it recognizes that personalization could significantly disrupt both healthcare delivery and innovation in the future (e.g., in R&D), the report primarily concentrates on AI’s impact on healthcare professionals and organizations, based on use cases currently available.

Finally, AI is still in its early stages, and its long-term consequences remain uncertain. Future applications of AI in healthcare, in innovation strategies, and in how individuals approach their health could be transformative. One could envision a future where population-level data from wearables and implants reshapes our understanding of human biology and drug mechanisms, enabling personalized, real-time treatments for everyone. This report, however, focuses on what is tangible today and what can drive innovation and adoption in the near term, rather than delving into the long-term future of personalized medicine. Amid the uncertainty surrounding the eventual scope of emerging technologies, certain short-term opportunities are apparent, as are practical steps that healthcare providers and systems can take to more quickly deliver AI-driven benefits to the populations they serve.

Understanding AI in Healthcare
In this report, AI in healthcare refers to technologies that influence care delivery, encompassing both improvements in current tasks and transformations driven by evolving healthcare demands or new processes to meet them. It also includes applications that enhance healthcare services, ranging from everyday operational efficiencies in healthcare institutions to population-health management and broader healthcare innovation. This definition is broad, covering areas such as natural language processing (NLP), image interpretation, and predictive analytics powered by machine learning. It demonstrates a continuum of AI solutions, starting from rule-based systems that encode clinical guidelines or protocols, which can later be strengthened with data-driven learning models.

AI has become a key focus for healthcare leaders, policymakers, investors, and innovators, including the European Union. Many countries, including Finland, Germany, the UK, Israel, China, and the US, have set ambitious goals for AI in healthcare and are investing substantially in AI research. The private sector also plays a crucial role, with venture capital funding for the top 50 healthcare AI companies reaching $8.5 billion. Technology giants, startups, pharmaceutical and medical-device companies, and health insurers are all actively participating in this emerging AI healthcare ecosystem.

Globally, AI growth patterns are evolving. The United States continues to lead in terms of VC-funded healthcare AI firms and the number of completed AI-related research studies and trials. However, the fastest expansion is occurring in Asia, particularly China, where major domestic corporations and tech companies offer consumer-focused healthcare AI solutions. For instance, Ping An’s Good Doctor, a leading online health-management platform, already serves over 300 million users. Europe benefits from extensive health data collected through national health systems and has notable strengths in research output, established innovation clusters, pan-European collaborations, and a unified approach to key AI aspects like ethics, privacy, and trustworthy AI. An emerging EU strategy seeks to leverage AI to benefit its population. Nevertheless, data silos, governance, access, and security challenges hinder full adoption. While European investment and research in AI are strong collectively, efforts remain fragmented at the national or regional level. Overall, there is significant potential for AI in EU health systems, but its practical impact is still limited. Surprisingly, 44% of healthcare professionals surveyed—even those engaged with healthcare innovation—reported never participating in the development or deployment of AI in their organizations.

Expanding Use Cases
Although questions remain about what is genuinely achievable with AI in healthcare today, this report examined 23 active applications and provided 14 case studies of AI already in use. These examples demonstrate AI’s broad impact: from tools that empower patients to manage their own care, to online symptom checkers and e-triage AI systems, to virtual assistants performing hospital tasks, to devices like a bionic pancreas for diabetes management. Some solutions optimize healthcare operations, such as scheduling and bed management, while others enhance population health by predicting hospital admission risks or aiding in early cancer detection, potentially improving survival rates. AI also contributes to healthcare R&D and pharmacovigilance. While many solutions remain small in scale, their growing implementation at the system level signals an accelerating pace of change. In most cases, the question is no longer whether AI can be effective, but rather how to maximize its potential while improving user experience and encouraging adoption.

We are still in the very early stages of understanding AI’s full potential in healthcare, particularly in terms of personalization. Nonetheless, interviews and survey responses suggest that AI in healthcare is likely to scale through three main stages, based on current solutions and ideas in development.

Initially, AI is expected to target routine, repetitive, and largely administrative tasks that consume significant time for doctors and nurses, streamlining healthcare operations and encouraging adoption. This first stage also encompasses AI applications in imaging, which are already implemented in fields like radiology, pathology, and ophthalmology.

The second stage is anticipated to feature AI tools that facilitate a shift from hospital-centered care to home-based care, including remote monitoring, AI-driven alert systems, and virtual assistants, empowering patients to take greater control of their health. This phase may also see broader deployment of NLP solutions both in hospitals and at home, as well as expanded AI use across more specialties such as oncology, cardiology, and neurology, where progress is already underway. For this stage, AI must be more deeply embedded in clinical workflows, requiring active involvement from professional organizations and healthcare providers. Successful implementation will depend on well-designed, integrated solutions that leverage existing technologies in new ways. Growth in AI adoption during this phase will be driven by both technological advancements (e.g., deep learning, NLP, connectivity) and organizational changes in culture and skills.

In the third stage, AI is expected to be increasingly applied in clinical practice, supported by evidence from clinical trials, with an emphasis on enhanced and scalable clinical decision-support tools. By this point, the sector will have learned from earlier attempts to integrate such tools and adapted its mindset, culture, and expertise accordingly. Ultimately, AI is expected to become a fundamental part of the healthcare ecosystem, influencing everything from learning and diagnostics to care delivery and population health management. Achieving this potential in European healthcare will require integrating broader datasets across institutions, implementing strong governance to maintain and improve data quality, and building greater trust among organizations, clinicians, and patients in AI technologies and their associated risk management.

Dr. Tara works as a pathologist, examining hundreds of samples daily. Recently, she began working with an AI-based screening system designed to flag specimens that may indicate cancer risk, sending only those cases to specialists for detailed review. At first, Dr. Tara was skeptical about the AI’s capabilities, but over time, she recognized its value as an impartial second opinion. Today, the AI system allows her to handle more samples efficiently while focusing her expertise on the cases that truly require her attention, boosting her overall productivity.

Beyond pathology, artificial intelligence is increasingly being applied across healthcare. Its ability to analyze vast amounts of medical data—including patient records, clinical notes, and treatment histories—provides healthcare professionals with rapid access to crucial information, helping them make faster decisions. For example, if a doctor encounters a patient with unfamiliar symptoms, they no longer need to order numerous tests. Instead, they can access a global database, locate similar cases, use AI to sift through relevant information, and arrive at a diagnosis much more quickly. Diagnosis is often the most complex aspect of medicine, as doctors rely heavily on observable symptoms. But in cases where symptoms manifest late, such as Alzheimer’s, AI can detect subtle early indicators of disease, increasing the chances of timely intervention and recovery. A notable example is AI systems that identify Tuberculosis from simple chest X-rays, enabling early treatment and often leading to positive patient outcomes.

AI also plays a transformative role in drug discovery, extending far beyond basic technological support to significantly reduce time and costs. Developing a new drug traditionally takes 10–15 years from initial research to prescription, often exceeding both projected schedules and budgets. Today, AI can predict potential side effects of new drugs and generate valuable insights based on previously analyzed data, streamlining the development process and lowering overall expenses.

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Not long ago, many applications of AI seemed unimaginable, but today they are rapidly becoming a reality. One of the most intriguing advancements in AI for healthcare is “explainable” artificial intelligence—a system that can clarify its decisions and actions to human users. Recently, Hitachi developed a system that assists doctors in predicting the likelihood of hospital readmissions within 30 days for patients with heart failure. This technology identifies patients suitable for readmission prevention programs after discharge, provides explanations for their high-risk status, and helps lower their chances of returning to the hospital. By bringing such groundbreaking AI solutions into practical use, Hitachi is leading the way in healthcare analytics.