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Artificial Intelligence is top of mind for healthcare decision makers, with VC funding for firms focused on the sector topping $8.5 billion and major tech companies, pharmaceutical and medical-devices firms and health insurers investing in a variety of AI initiatives.
Artificial Intelligence is Changing the Patient Experience
From robotics in hospitals to predictive software used to diagnose rare diseases, AI has become a vital tool in many aspects of healthcare. Many clinicians now rely on AI-powered tools to help them do their jobs, including analyzing patient data to help identify potentially dangerous or life-threatening symptoms, diagnosing patients using medical images such as X-rays or CT scans, and helping optimize healthcare resources in the emergency department by forecasting patient demand and suggesting appropriate therapies.
For healthcare providers, introducing AI into clinical workflows presents both challenges and opportunities. Managing change is critical, and interviewees stressed the importance of identifying the right solutions that support rather than antagonize practitioners and truly augment rather than replace their ability to deliver patient care. This could include prioritizing AI solutions that remove or minimize the time spent on routine administrative tasks—which can take up to 70 percent of a physician’s workday—and CDS tools that facilitate activities physicians see as core to their professional role, such as clinical diagnosis.
As a result of rising consumer focus, patients expect healthcare organizations to offer them convenient access to personalized and responsive care. This includes leveraging AI-powered technologies that support remote monitoring, alerting systems and virtual assistants to manage their health in the comfort of their homes. It also involves using ML to identify and reach out to patients who haven’t scheduled annual wellness visits, with the goal of reengaging them through preferred communication channels and encouraging them to do so via streamlined, AI-driven scheduling options. By adopting modern technologies in healthcare, organizations can significantly enhance patient care while improving operational efficiency across the board.
Diagnosing disease is a complex and challenging task, and the healthcare industry has historically relied on a combination of clinical experience and sophisticated laboratory tests to ensure accurate and timely diagnoses. ML can automate many parts of the diagnostic process by interpreting large amounts of patient-specific information, such as medical records, genetic information, and social/lifestyle factors, to generate predictions about disease progression and identify potential risk factors that can be addressed with preventive care interventions.
Ultimately, the goal of AI is to improve outcomes and reduce costs by providing actionable insights based on data. But the key to success is ensuring that the technology is transparent and adheres to ethical guidelines.
Artificial Intelligence is Changing Care Delivery
In healthcare, AI can help enhance clinical practice. It can diagnose diseases, develop personalized treatment plans and assist clinicians with decision-making. It can also support medical education, remote monitoring and patient empowerment through self-care. Moreover, AI can improve the day-to-day life of practitioners by reducing workload and helping them focus on patients. It can even speed up diagnosis and get life-saving treatments to market faster.
Despite its potential, the integration of AI into healthcare is not without challenges. The most significant barrier is a lack of resources needed to build and deploy AI solutions. This is particularly the case for small to medium-sized healthcare organizations that cannot attract and retain AI talent, and who may struggle with the cost of acquiring, developing and running AI applications. Larger healthcare organizations are better equipped to invest in AI and work in partnership with technology vendors. This could be accomplished through collaboration in innovation clusters or centers of excellence, and through regional and public-private partnerships.
Furthermore, there are a number of cultural and ethical barriers to the use of AI in healthcare. While some healthcare professionals are reluctant to use AI in their practice, others are open to it and see the benefits of using it to improve patient outcomes, boost efficiency, and provide greater access to personalized care. Trust-building and education are essential to this, as is addressing challenges like data quality, privacy, bias and the need for human expertise.
Lastly, the digitization of healthcare systems and processes is necessary to enable the introduction of AI. This was identified by interviewees as a critical first step in the process and was reflected in survey responses from healthcare system respondents. MGI research shows that healthcare is one of the least digitized sectors, and if basic digitization is not undertaken before embarking on an AI deployment, this will lead to frustration for staff and could derail adoption. Therefore, a top priority for healthcare leaders is to digitize systems and processes and implement the appropriate governance structures before implementing AI. This will include defining clear priorities, consolidating funding and creating incentives for healthcare organizations to collaborate on AI development.
Artificial Intelligence is Changing the Workplace
Artificial intelligence is transforming the workplace in healthcare by enabling practitioners to focus on what they do best: deliver quality care. From robots in hospital operating rooms to machine learning algorithms that comb through electronic health records looking for patterns to detect potential early signs of disease, AI is increasingly being deployed to augment human capabilities.
This is allowing doctors, nurses, technicians, therapists, and other healthcare professionals to save time on repetitive administrative tasks by automating processes or by using generative AI to write reports and perform tasks like verifying documents, transcribing telephone calls, or answering simple questions like “what time do you close?”
Moreover, AI-powered software is analyzing large datasets to predict the likelihood of a patient developing a certain condition or diagnosing a rare disease, tracking how contagious diseases spread through a population, or discovering new applications for drugs. It’s even predicting whether patients are taking medications that might interact negatively with one another.
The future of healthcare will require a workforce trained in integrating technology into their workflows. This means changing the way that healthcare education is structured, shifting the emphasis away from memorizing facts to entrepreneurship, innovation, and continuous learning. It will also mean training all employees, including non-clinical staff, on the use of digital tools and how to implement AI into their work.
It will also mean setting up centers of excellence for AI in healthcare, a model that can consolidate scarce talent and accelerate the adoption of technologies. It will be necessary to support public-private partnerships that can help establish regional or global leadership in this area. These centers can lead the design and development of new AI systems for healthcare, paving the way for their implementation in national health systems. They will be key to tackling challenges that may slow the deployment of AI in healthcare, such as limited access to data, the need for training, and differences in existing IT systems. They will also ensure that the healthcare sector is better equipped to manage complex, high-risk AI systems. MGI research shows that the healthcare sector is among Europe’s least digitized sectors and needs to prioritize digitization and set up governance to enable the full deployment of AI.
Artificial Intelligence is Changing the Payment Model
Artificial intelligence is now top-of-mind for healthcare decision makers, investors and innovators, with big tech firms, startups, pharmaceutical and medical-device companies and health insurers all engaging with the nascent AI healthcare ecosystem. The report provides a unique view from the frontline, hearing directly from a range of healthcare professionals and entrepreneurs on what is working and where the real potential, opportunities and barriers lie.
Across Europe, there is significant interest in using AI for healthcare, with numerous organisations already developing or piloting solutions. These range from digital symptom checkers and e-triage AI tools, to virtual agents that help patients manage their care, through to intelligent telehealth devices and robotic systems for carrying out tasks in hospitals.
While the technology is still evolving, there are some clear early wins – enabling patients to manage their care more independently and effectively (eg by helping them understand their condition better or by allowing doctors to remotely monitor and treat their patients via remote cameras, MRIs or X-rays). Other use cases include fraud prevention (a massive USD 380 billion/year problem in healthcare, raising medical premiums, out of pocket costs and even hospital admissions), and optimising healthcare operations, R&D and pharmacovigilance through AI.
Many interviewees emphasised the importance of ensuring that AI solutions are designed and developed with quality in mind, which will be key to their successful adoption and integration into the workflow. This includes focusing on simple and intuitive interfaces, ensuring that the right data is collected, stored and structured and enabling healthcare staff to easily integrate data into their workflow, while addressing public concerns about how medical information is used.
The development of centres of excellence, bringing together scarce AI talent in high-profile, agile networks that can quickly design and implement new capabilities and spearhead their implementation in healthcare systems, will also be crucial to accelerating the spread of these technologies. These can act as catalysts for broader change, driving innovation and introducing new approaches across national health systems. They will help to define a regional or national strategy for AI healthcare, setting out a medium and longer-term vision, goals, specific initiatives and resources, and establishing performance indicators for success.
