Artificial intelligence (AI) is a machine’s ability to perform cognitive functions usually associated with human decision-making.
Automation of human-dependent processes and actions presents an overwhelming challenge to UK healthcare leaders.
The potential benefits of automation through the adoption of AI for patient outcomes and operational efficiency are colossal, but navigating complex technological and ethical issues is time-consuming and costly. And this raises an important question. How should leaders approach the challenge of AI transformation?
In this post, we’ll look at the current state of AI in healthcare, why AI is important and the array of benefits it can provide, and how the sector is adapting to cutting-edge technology.
The state of artificial intelligence in healthcare
Though adoption is still in an early stage, AI is seeing increasing integration into healthcare settings. Many pioneering initiatives show the transformative potential of AI and advanced technology.
Surrey Hospital in Canada is one example. It used BIM (building information modelling) to restrict building collisions, cut build time (31 months) and increase energy efficiency by 40%.
Humber River, the first fully digital hospital in North America, is another case study that illustrates the significant potential of AI adoption. Billed as the world’s first fully digital hospital, Humber River has automated everything from the nurse alarm system to the window panels.
AI in healthcare NHS settings has also seen several important applications, including analysis of X-rays, support of people in virtual wards, and interpreting brain scans.
Transforming patient care and operations through AI
AI healthcare applications are present in six key areas: prevention, detection, diagnosis, treatment, palliative care and operations and training.
- Prevention: AI and the Internet of Medical Things (IoMT) widen the functionality of consumer health apps, meaning people can manage their health more effectively. AI for health prevention is already reasonably well-established, in part due to the presence of private companies innovating in this space.
- Detection: AI can improve the accuracy of early disease detection. Cancer mammograms are one well-studied example. Others include brain scans, CT scans, skin disease categorisation and risk assessment of sudden cardiac attack.
- Diagnosis: AI diagnosis tools and technologies (IBM’s Watson and Google’s DeepMind are two well-known examples) can process substantial amounts of health data, supplementing the human diagnosis process and increasing accuracy.
- Treatment: Technology and robots already play a significant role in healthcare, including microsurgery, rehabilitation and the management of long-term illnesses and disabilities. AI is further strengthening this role by increasing the functionality of existing systems.
- Palliative care: AI use in the later stages of life has an array of applications. For example, AI-powered robots may help maintain independence for the elderly, improve social interactions and reduce reliance on institutional care.
- Operations and training: AI automation expands the scope of existing non-manual workflows in healthcare settings, which tend to be fairly simple and rule-based. AI can also expand medical training through simulations and adaptive learning programmes.
Overcoming challenges and ethical considerations
AI adoption presents three main ethical challenges, largely focused on inclusivity and defining the proper role of human intervention.
- Equity of access: There is a risk that AI technologies will widen health disparities if they are accessible mainly to wealthier or more technologically advanced segments of the population.
- Human accountability: Determining liability in cases of medical errors involving AI is complex. Questions about whether responsibility lies with the healthcare provider, the AI developer or an algorithm itself are still to be adequately addressed.
- Algorithmic clarity: AI's decision-making process can be opaque. This phenomenon is sometimes referred to as "black box algorithms", where it is not clear how decisions are made. This lack of transparency can complicate accountability, particularly when errors occur or when AI-driven decisions need to be explained or justified. A lack of technological understanding among healthcare professionals can further exacerbate issues.
Looking ahead: The future of AI in healthcare
What does the future hold for AI services in healthcare?
Along with improvements in patient and operational outcomes, we believe three areas will see significant activity: ethics, everyday usage fuelled by training and innovations at the forefront of current capabilities.
- Addressing ethical challenges: It will be essential to establish transparent mechanisms for algorithmic accountability and patient data usage.
- Training and growing adoption: Advanced simulation tools and cross-disciplinary educational strategies will help to integrate AI smoothly into healthcare workflows. We expect these initiatives to be supported by continuous professional development.
- Potential for breakthroughs: Developments in neural networks and machine learning algorithms will likely propel significant innovations in diagnostic procedures and clinical interventions in the coming decade.
Conclusion
To say AI has potential is an understatement. The shifts and breakthroughs AI is providing and could provide are immeasurable.
Reducing the risk and impact of human factors through AI delivers integrity in processes, improving quality, reliability and efficiency.
Healthcare leaders are the people who will drive this shift, improving patient care and relieving operational burdens across an array of measures.
Those in a position to take advantage of these opportunities should waste no time in fostering understanding and implementation across their entire organisations.
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