Artificial intelligence to transform healthcare – are we ready for health 2.0?
With a market projected to reach $70 billion by 2020, AI is dominating the debate over technologies and is expected to deeply transform consumer, enterprise, and government markets. In many sectors, AI is already a reality: online customer support, smart home devices, video games, spam filters are only a few AI applications that we are currently using. AI makes our lives easier, but it also has the potential to help us to solve some of the world’s biggest challenges: from treating chronic diseases to anticipating cybersecurity threats.
Health is one of the areas where AI can make a real difference in people’s lives. AI medicine has a tremendous potential in radiology, prognostics, diagnostics, surgery, resource allocation and treatment recommendations. So why has the health sector been slower than other industries in adopting IoT technologies?
The future of AI depends on trust and education
AI real-life implementation is still facing some obstacles, starting from some crucial infrastructure barriers, such as non-uniform electronic forms, storage capacities and sharing facilities across healthcare settings. Infrastructure issues have straightforward (and costly) solutions but these issues are far from being the only challenge for AI.
Two keywords are at the core of the delayed uptake: trust and education.
When it comes to the uptake of new technologies impacting so much in our lives, everything is about trust.
AI’s uptake depends upon safe access to big data and patient records. But the latter is not possible without patients’ trust. Trust is also very much linked to the ethical challenges that AI solutions bring, from equality to impact on human behaviour and security. No one should be left behind, the benefits brought from AI advancement should foster public health as a whole, not just benefit a few businesses or a small number of patients.
Likewise, in order to secure trust in the development and use of AI technology, policymakers must ensure the security of potentially sensitive data and provide clear guidelines on who is responsible when AI is used to support decision-making processes and when errors or complications occur.
Equally important for AI uptake is education. New skills are required as we move toward a new digital era of care. AI innovation can be enabled only if healthcare professionals’ education and patients’ literacy are continuously supported. The interface between clinicians and machines needs to be effectively and skilfully managed. Medical staff must have a basic understanding of how data is being collected and analysed through AI applications and other digital tools.
Building trust and fostering education
A human element should remain if we want to build trust on AI solutions. I believe that physicians simply cannot be substituted by machines. However, AI can definitely assist the healthcare workforce in making better clinical decisions, or even in replacing human judgement in certain tasks. Good examples of this can be found in image-recognition and radiography, where AI algorithms cover important areas from cancer detection, radiology reporting and analytics to scheduling and patient screening.
AI solutions in radiology showcase the real challenge behind AI literacy: medical professionals must learn to better delegate repetitive, lower-level cognitive functions in order to focus more on higher-level thinking.
Furthermore, medical students should learn to apply big data concepts to better quality of care, to support research and to improve patients’ experience. Medical education must move beyond the foundational clinical sciences by giving more attention to the alignment of humans and digital solutions and to data-driven outcomes. Ultimately, education is essential to feel safe and in control, both for patients and healthcare professional. We cannot trust or use something that we do not know.
AI is not the future, it is the present. Its solutions are already transforming healthcare, as other sectors, presenting new regulatory and technical challenges that EU member states should quickly meet to make AI investments cost-effective and to make AI technology work for everyone.
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About the author
Prof Damien Gruson is Chef de Service at Cliniques universitaires Saint-Luc and coordinator of the My City-Lab Project. My City-Lab is a project financed by the European Regional Development Fund (ERDF) which aims to integrate the innovation of laboratory medicine and mobile health. The scope of the project is to facilitate access to laboratory tests as part of a collaborative approach to ambulatory care of a chronically ill individual, as well as to contribute to the dynamic monitoring of patients with chronic diseases.