Artificial Intelligence is catalysing transformation at pace across every industry, redefining the way both public and private organisations operate and the way their stakeholders (including employees, customers, citizens and patients) engage with them. In healthcare, the advancements of AI technologies are revolutionising the way patients are treated, doctors and nurses work, and how diseases are diagnosed and even understood, creating many opportunities for innovation. With the potential use-cases for AI integration constantly expanding, it is rapidly becoming a core pillar in the future of healthcare delivery, improving patient outcomes and health equity. However, with its application into healthcare, there are also potential ethical and social challenges that are critical for us to be aware of and address. These challenges include addressing biases in AI algorithms and avoiding discrimination, ensuring human autonomy and oversight, addressing data quality and security, providing technical robustness across the digital ecosystem, defining accountability, promoting transparency, determining data ownership and governance structures, engendering trust, ethical adherence, and regulatory compliance.
Mecomed in collaboration with PwC Middle East ran a workshop examining the impact of AI in healthcare. This included representation from SMEs across the whole healthcare ecosystem to address the most critical issues and provide a series of recommendations and actionable insights as we work together to redefine the future of healthcare.
To achieve ethical, reliable, robust, secure, and sustainable AI in healthcare, we need an appropriate and agile policy framework that encourages innovation and research partnerships, builds a data culture that respects personal data privacy and security, supports education and upskilling in the healthcare and regulatory workforce and encourages international cooperation, alignment and harmonization.
The policy framework on AI in healthcare should take into consideration the need for global harmonization of AI regulations, ethical and societal considerations and data quality and management issues. Achieving consistency across regulatory agencies and aligning horizontal approaches with vertical legislation will prove as crucial as the establishment or adoption of specific internationally recognized principles for AI.