Ranking Issues Challenging AI Adoption In Healthcare
What Is Really Holding AI Back?
There is growing pessimism about the potential of AI in the health industry. With the technology in its infancy, it is easy to pick holes in its capacity today. The long-term potential is clear but how do we get there quickly in an ethical way?
AI in the health industry is poised to solve a range of long-standing and costly problems that have a real impact on people's lives. Though there are serious questions to be asked about the applicability of AI across multiple domains and verticals, both patient-facing and operationally.
This article looks at some of the areas where AI will be most influential and ranks the complexity the issue faces out of 10. 1 being ‘low complexity, easy to implement ‘and 10 being ‘high complexity, very challenging to implement’
Personalised Health Recommendations — 3/10
Generative AI is being used effectively in apps and tools that provide personalised health recommendations based on user data (e.g., lifestyle, diet, exercise habits). These AI-driven systems are relatively easy to implement and use, and they offer real value to users by helping them manage their health proactively. While challenges remain in ensuring accuracy and avoiding bias, the technology is already quite effective at the consumer level.
Staff Technology Adaptation — 4/10
Some staff will struggle to understand how to use AI effectively but the simplicity of AI and the advances in UI will create a fairly seamless transition. Standard procedures for upskilling will shift to the most important tools so AI will have plenty of attention given in L&D.
Clinical Co-Pilot — 4/10
Before we give AI diagnosis and treatment autonomy AI clinical co-pilots will support practitioners in decision-making efforts. We are seeing these implemented already with data showing highly effective use. These will only get more effective as practitioners learn how to get the best out of them and the AI gets better.
Patient Trust and Acceptance — 6/10
Once effective tests demonstrate the reliability of AI responses then the AI systems will quickly become trusted by patients. Those with simpler issues will be seen quicker and for less cost. More complex issues will take time to develop trust but that will come with every correct diagnosis.
Cost and Resource Constraints — 7/10
This one was definitely harder to assess. Developing, implementing, and maintaining generative AI systems in healthcare can be expensive. Many healthcare providers, particularly in low-resource settings, may not have the financial means or technical expertise to adopt these technologies, creating disparities in access to AI-driven healthcare solutions. Despite this, in just a few years the costs have come down significantly and scaling laws will suggest this trend continues.
Liability and Accountability — 8/10
I see this issue dragging out for a while even as the quality and impact of the AI systems improve. Determining who is liable when AI-generated recommendations lead to adverse outcomes is a significant concern. The complexity of AI systems can make it difficult to assign accountability, which can lead to legal and ethical challenges, especially in malpractice cases.
Data Privacy and Security Concerns — 9/10
Even today we have many instances of patient data being mishandled indicating clear security concerns. With AI autonomously handling and using data with groups looking to take advantage of weak security, patients and health organisations will need greater assurances of effective data privacy and security.
Conclusion
I remain incredibly excited about the potential of AI in the health industry.
For every issue, there are multiple companies developing effective solutions to these problems. The potential of AI is significant which means there are strong incentives to make AI work effectively across multiple industry domains.
Some issues will take more time than others to mitigate. The market will drive solutions as businesses see the opportunity for significant cost savings and enhanced patient/client outcomes.
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