AI solutions are trained on data sets that come together to build pattern-based models. In healthcare, those models need to tick two hugely important boxes: they must be explainable, and they must demonstrate cross-denominational equity in terms of the data they’re using. 

The people using new AI healthcare tools need to know what they can do, how they work, and where their limitations lie. This includes always having trained people in a position to maintain oversight – and overrule AI output if needed. But it also means getting patient buy-in, and ensuring everyone across the board that AI won’t replace genuine human care. 

Successful integration with existing systems is a tightrope walk that requires incredibly forward-thinking data governance, security, and interoperability processes. And any healthcare, Pharma, or MedTech body looking to deploy an AI tool needs to develop a framework that can scale without compromising on these tenets. 

The breakdown

AI systems trained on datasets that do not represent diverse populations can produce skewed results – with outcomes that might exacerbate existing health disparities.  

Building patient trust in AI requires addressing these biases and ensuring that AI systems are transparent and explainable.

The solution(s)

AI systems must be trained on diverse datasets that accurately represent the populations they are intended to serve.  

Additionally, developers must implement robust testing and validation processes to identify and mitigate any biases that may emerge during AI learning.

The breakdown

Understanding how AI works, alongside its capabilities and limitations, is a crucial skill for healthcare professionals at all levels – including clinicians, administrative staff, IT professionals, and healthcare managers who interact with AI systems.

There is also fear that AI might replace specific jobs, leading to pushback from staff and patients, even when the technology is intended to augment rather than replace human capabilities.

The solution(s)

Equip staff with the knowledge and skills to work effectively with AI technologies – which includes understanding how to interpret AI outputs, recognising when human judgement is necessary, and refining AI systems in practice.

Curriculum enhancements that include courses on AI-driven diagnostics, personalised medicine, and the use of AI in clinical decision support.  

Addressing these concerns will also require a clear emphasis on AI's complementary role in supporting, not supplanting, human healthcare providers. 

The breakdown

Healthcare data is often fragmented, residing in silos across different institutions and systems, and lacking in standardised data formats.

Hospitals and clinics vary widely in technological maturity – a disparity that necessitates a highly adaptable AI framework.

Healthcare data is among the most sensitive types of information, encompassing medical histories, treatment plans, personal identifiers, and financial details; a data breach in healthcare can lead to identity theft, financial loss, and even endanger patient safety.

The solution(s)

Healthcare organisations must implement data governance policies that define standards for data entry, validation, and maintenance. This includes regular data audits as well as guidelines for the ethical collection, use, and sharing of data.  

Healthcare organisations should adopt a phased approach to AI implementation, starting with well-defined, lower-risk use cases.

A robust data infrastructure is paramount. This infrastructure includes the systems and technologies that enable health data storage, processing, and transmission across various platforms and stakeholders.