Artificial Intelligence (opens in new tab) (AI) and AI (opens in new tab)-mediated technologies in medicine and healthcare have experienced an extraordinary evolution, starting as computer programs to support the analysis of medical images to its integration in almost every clinical and organizational area. It has the potential to re-wire healthcare delivery, augmenting human capabilities in unprecedented ways.
Balakrishna D R is Senior Vice President and Service Offering Head for ECS at Infosys (opens in new tab).
AI can improve care outcomes, patient experiences, and access to healthcare services while empowering healthcare systems to provide additional and better care to more people. It can also support the faster delivery of care, mainly by accelerating diagnosis time and saving precious time and lives.
One of the applications of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. AI programs are applied to practices such as diagnostic processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. AI algorithms can also analyze large amounts of data (opens in new tab) through electronic health records for disease prevention and diagnosis, furthering their utility in early detection and outbreak prevention of communicable diseases.
Medical institutions worldwide are embracing AI to improve operational initiatives that increase cost saving, improve patient satisfaction, and meet their staffing and workforce needs. AI startups are also in various stages of developing technologies that help healthcare managers improve business operations through bettering utilization, decreasing patient boarding, reducing the length of stay, and optimizing staffing levels.
Healthcare in Europe
Over the past century, average life expectancy at birth has risen from less than 50 years to 80.9 years on average for EU member states. As the population ages and longevity increases, healthcare systems around Europe face a growing demand for services, rising costs of delivery, and significant challenges in building the workforce required to deliver care. Market is driven by a combination of convergent forces – aging populations, increasing patient expectations and lifestyles stemming from rapid urbanization, modernization and globalization, and the accompanying changes in health risk factors.
The implications for an aging population are especially grim. By 2050, 1 in 4 Europeans will be over the age of 65. This demographic shift, accompanied by the risk factors mentioned above, translates to chronic conditions becoming more common leading to increased demand for healthcare. This requires healthcare systems to focus on episodic care based on hospital admissions to long-term proactive management of chronic care. It also requires a different set of skills and a strong culture of collaboration (opens in new tab) between physicians across specialties and other healthcare practitioners.
Managing comorbid patients with complex needs is expensive for health systems. In 2018, healthcare expenditure in Europe’s advanced economies ranged between 8.8-11.2% of gross domestic product and was likely to continue rising. Staff shortages and skill gaps are also limiting healthcare systems. AI can transform healthcare delivery and help meet some of the economic and social challenges mentioned through predictive models and intelligent automation.
State of adoption in Europe
Europe is quickly emerging as a significant global hub for healthcare AI, though lagging behind the US. Example, an Oxford-based AI-driven drug discovery company, Exscientia discovered a novel first-in-class small molecule that Sanofi is currently progressing. More recently, the company announced it was working with US partners to apply its technology to searching for drug candidates to treat Covid-19. Several AI-first drug discovery startups are raising mega funds and leveraging platform technologies to develop and commercialize new discoveries.
The EU is already making striding steps in regulation and setting standards around ethics, data security, and confidentiality and setting up broader initiatives that include strategic investments, research contributions and growing funding. However, the adoption of AI in mainstream healthcare in Europe has been relatively slow.
Europe benefits from the vast troves of health data collected in national health systems. It has significant strengths in the number of research studies, established clusters of innovation, and pan-European/continental collaborations. For instance, an AI-powered screening mammography was designed in a collaborative effort between UK- and US-based organizations that reduces false positives and false negatives. It helps to have a pan-European approach to building an emerging strategy on ensuring AI delivers the derived advantages. Yet, challenges remain.
Issues around data will always be at the heart of successfully promoting AI solutions. Critical data-governance, access and security issues still need to be clarified, delaying further adoption. European investment and research in AI are substantial when grouped but fragmented at the country or regional level. Health systems need to take a systematic approach to develop common data standards and processes to maximize the value of existing data. As one of the least digitized economic sectors, this remains a challenge. Healthcare providers and AI companies need to put robust data governance, ensure interoperability and standards for data formats, enhance data security and bring clarity to consent over data sharing for gaining trust and confidence of stakeholders to create large healthcare data lakes for AI-driven innovations.
The urgent case for coordinating a mechanism to integrate AI into contemporary healthcare received a boost with the onset of the COVID-19 pandemic. Researchers that include several Europe-based companies such as Healx, BenevolentAI, Innoplexus, Evaxion Biotech have been working on developing vaccines with speed and scale due to AI-powered R&D. One critical use case has been molecular simulations - trying to figure out how to discover therapeutic compounds and potential vaccines in a simulation environment with machine learning (opens in new tab).
AI-powered solutions have made some steps towards addressing key issues but have yet to achieve its full potential in the healthcare industry. Europe could indeed look forward to unlocking the unrealized value of AI towards augmenting clinical resources and ensuring optimal patient outcomes with effective policies, regulations and strategies that address key challenges.
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