AI in Healthcare: Overcoming Barriers for Transformational Benefits
Artificial intelligence (AI) has sparked excitement and anticipation across industries, but its potential to transform healthcare is particularly profound. In Europe, analysts predict that deploying AI could save hundreds of thousands of lives each year, while in America, it could save money, shaving $200bn-360bn from overall annual medical spending.
From smart stethoscopes and robot surgeons to the analysis of large data sets and AI-powered patient consultations, opportunities abound in healthcare. Studies have shown that AI systems can enhance diagnostic accuracy, disease tracking, improve the prediction of patients’ outcomes, and suggest better treatments.
AI can also boost efficiency in hospitals and surgeries by automating tasks such as medical transcription and patient monitoring, and by streamlining administration. It may even accelerate the time it takes for new drugs to reach clinical trials. New tools, such as generative AI, could further enhance these abilities.
However, despite these promising advancements, the integration of AI into healthcare has been slow, and the results have often been mediocre. There are both valid and unfounded reasons for this slow adoption.
Barriers to AI Adoption in Healthcare
Data Fragmentation and Privacy Concerns:
AI systems learn by processing vast amounts of data, an asset that healthcare providers have in abundance. However, health data is highly fragmented, and strict regulations control its use. Governments recognize the importance of protecting patient privacy, but patients also deserve better and more personalized care.Improving the accuracy and reducing bias in AI tools requires training these systems on large data sets that reflect patients’ full diversity. Secure ways to allow health data to move more freely would greatly benefit AI development. Patients should also have the right to access their own records in a portable, digital format.
Regulatory Challenges:
In many countries, the governance of AI in health, as in other areas, is struggling to keep up with the rapid pace of innovation. Regulatory authorities may be slow to approve new AI tools or may lack capacity and expertise. Governments need to equip regulators to assess new AI tools and fill regulatory gaps in the surveillance of adverse events and the continuous monitoring of algorithms to ensure their accuracy, safety, effectiveness, and transparency.International collaboration is crucial to learn from each other and create minimum global standards. A less complex international regulatory system would also foster a market where small companies can innovate. Poorer countries with less developed health infrastructure have much to gain from introducing new AI tools, such as AI-powered portable ultrasound devices for obstetrics.
Institutional and Incentive Misalignment:
AI promises to cut medical costs by assisting or replacing workers, improving productivity, reducing errors, and flattening or reducing spending, all while improving care. However, saving money using innovation is not always straightforward.Health systems are typically designed to improve care, not cut costs. New technology may account for a significant portion of the annual growth in health spending, and layering on new AI systems can increase costs and complexity. Redesigning processes to make efficient use of AI may face resistance from patients and medics.
Furthermore, many health systems, such as America’s, reward the volume of work, providing little incentive to adopt technologies that reduce the number of visits, tests, or procedures. Even publicly run health-care systems may not prioritize cost-reducing technologies if they perceive it could lead to a smaller budget in the future.
Unless governments can change these incentives, AI adoption will likely increase costs rather than combining better treatment with new efficiencies.
The Role of Governments and Regulators
Much of the burden for boosting AI in healthcare falls on governments and regulators. They need to:
– Equip regulatory agencies to assess and approve AI tools promptly and effectively.
– Fill regulatory gaps in monitoring adverse events and ensuring the accuracy, safety, effectiveness, and transparency of algorithms.
– Facilitate secure data sharing and data portability for patients’ full digital records.
– Create minimum global standards through international collaboration.
– Fund dedicated schemes to test and deploy new AI technologies in healthcare.
The Role of Healthcare Companies
Healthcare companies also have a crucial role to play:
– Ensuring the safety, reliability, and accountability of their AI products.
– Keeping humans in control of AI systems, acknowledging their limitations and potential for bias.
– Collaborating with governments and regulators to develop responsible and ethical AI practices.
Overcoming the obstacles to AI adoption in healthcare is essential to unlocking its full potential. By addressing data fragmentation, privacy concerns, regulatory challenges, and misaligned incentives, governments, regulators, and healthcare providers can pave the way for AI to transform medical practices, improve patient outcomes, and ultimately save lives.
