‘Medical culture has begun to change, but the change is by no means complete. AI developers need to tackle the issues both doctors and patients will have with the adoption of AI and build the right tools to facilitate the changing medical landscape’
AI in healthcare is no longer in the testing phase, there are plenty of existing applications. But, there are also considerable privacy implications that must be addressed
Before AI can truly transform healthcare, privacy and trust issues must be solved.
There is no doubt that AI is beginning to impact the healthcare industry. There is also no doubt that this impact will accelerate at an unprecedented rate.
No longer stuck in the laboratory, but now being used in real-life across the healthcare industry, AI is seeing real demand for AI in healthcare, often driven by patients and end users.
AI in healthcare: why is it important?
The introduction of AI into healthcare is important for several reasons. The main one, though? Scale. “With the multiplication of the population and the trailing skilled manpower availability, using AI will be the only way to scale services to match the mounting demand.
Technology (combined with other solutions, like Error! Hyperlink reference not valid.) will have a wide range of applications in everything from personal medicine to research, diagnosis and logistics.
But, despite a clear desire to integrate AI, it must be done correctly. And before it can effectively disrupt the sector, Lorica suggests that “various organisational and cultural changes need to be implemented. Importantly, doctors and patients need to develop a culture that allows AI to participate alongside normal practices.
Patient confidentiality
Before AI can truly transform the healthcare sector, the elephant in the room needs to be addressed: patient confidentiality or privacy.
Most data stacks hold personal information about almost every person living in the country, which means preserving privacy, collecting and cleaning data and data sharing [the key to AI success] is paramount. The issue of privacy is extremely pertinent in AI’s adoption. Patients need to be assured that their privacy won’t be abused when giving their medical histories to a computer, instead of traditionally hand written records.
Ensuring trust is the main challenge an AI-led healthcare sector must overcome in order to fulfil its potential.
Clinicians, administrators and regulators are rightly risk averse when it comes to implementing new technologies and we will only start to see AI services deployed at scale within healthcare when the industry has been able to demonstrate that these technologies are safe, reliable and trustworthy. The best way to do this is to develop appropriate benchmarking and evaluation standards for AI-based health technologies. This requires a collaborative effort between tech companies, policymakers and doctors — it is no easy feat but getting this right will be the key to unlocking the next phase of AI adoption within healthcare.
Everything begins with data
CTOs of healthcare providers and payers should not forget that everything begins with data.
For AI to succeed, every CTO should build the necessary foundational components and technologies for data acquisition, integration, storage, orchestration, lineage, governance, etcetera. That means a CTO will need to make the case for investing in the needed data infrastructure.
CTO’s should work with users and domain experts to set realistic expectations, particularly when it pertains to what a proposed AI system can do. For example, just because you have large volumes of data doesn’t mean you are all set. This data may need to have proper labels, or it might be bias and not representative of the population you are serving.
Data privacy and security are key to make AI data to work effectively.
