Healthcare providers

The four challenges blocking AI in healthcare – and how to solve them

Regulatory approval. Algorithmic bias. Human safety. Intelligent integration. These are the core challenges that any health-focused generative AI application must overcome. But doing so is a tall order… 

6 minutes to read

Implementing any new technology is tough. But with artificial intelligence both the stakes and the hurdles are much higher than usual. That’s especially true in healthcare, where human lives are on the line if errant technology makes even the smallest of slip ups.  

But here’s the thing: an AI transformation is coming to the medical world, and it’s going to upend just about every corner of the sector. The question, then, is obvious: how can those looking to deploy AI for healthcare solutions into their tech stack understand what those hurdles are, and take the right steps to leap over them? 

In Zühlke’s new report, Incremental adoption: a holistic analysis of AI’s future in healthcare, we’ve dug deep into the future of AI in healthcare to answer that very question.  

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Through a series of interviews with an international panel of experts and industry leaders, we’ve mapped the obstacles in the way of AI-powered healthcare tools – and outlined the processes and methodologies for mitigating them.  

Here’s a snapshot of what we’ve learned… 

AI: against all odds?

The challenges developers, researchers and healthcare providers all need to clear when thinking about the use of AI in medicine fall into four categories:

  • Ethical and security
  • Algorithmic
  • Human
  • Technological

Each one of those categories acts as a locked door between right now and what’s next, and each one is a blocker that needs its own robust solution:

  • Ethical and security challenges

    You can’t put any new technology to use in pharma, MedTech, or Healthcare without passing stringent regulations. But this issue is compounded by the fact that those regulations often lag behind the technology in question. 

    Dan Klein, Global Chief of AI & Data at Zühlke, describes this as a notoriously wide-reaching problem: 'At the moment, nobody has gotten any Gen AI applications through regulatory approval – and it's looking challenging as to how that route to approval would look like'.  

  • Algorithmic challenges

    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. 

  • Human challenges

    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. 

  • Technological challenges

    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. 

From regulatory and algorithmic struggles ('There isn't a very good regulatory framework, and there needs to be an explainability hierarchy') to human-centric challenges ('We need engineering-doctor hybrids to make these tools'), interviewees in our research unilaterally cited risks that fall within these four groups. 

The result is a kind of jadedness about AI’s outlook in the sector. 'We hear a few interesting examples of new technology', said one interviewee, 'but it all feels a long way away from the IT we have now'. 

The blueprint for better implementation

So how can we make these dominoes fall? The simple answer is robust processes that begin at the developmental level. With the right frameworks and planning in place, AI solutions can clear regulatory approval, work fairly and, with motivated users and decent policies, they can deliver tremendous added value.

But each part of the puzzle needs equal thought, care, and process development. Here’s a quick field guide to navigating the core problems blocking AI in healthcare:

The breakdown

The healthcare industry is one of the most heavily regulated sectors. For AI technologies, regulatory challenges are acute because tools don’t often fit neatly into existing frameworks – and regulations are playing catch-up with the technology.

The solution(s)

Medical regulations that are robust enough to handle the ever-evolving nature of AI technology will need to proliferate at the government level, but with the help of other bodies (like nonprofits) that can monitor new developments. 

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.  

See the bigger picture

Listed above are just some of the strategies we outline in our new whitepaper, Incremental adoption: a holistic analysis of AI’s future in healthcare, and they represent a broad-strokes approach, rather than pragmatic next steps. 

Moreover, leaping deployment and development hurdles is just one part of the wider ecosystem and landscape of AI in healthcare. To see the bigger picture – and to learn where the AI healthcare hype stops and its real-world adoption can start – get the full report:

AI in Healthcare: Transforming Potential into Reality

AI has the power to revolutionise healthcare, but how can organisations navigate its complexities and unlock true value? Discover how to unlock AI’s full potential for healthcare.

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