The remarkable speed at which text-based generative AI tools can complete high-level writing and communication tasks has struck a chord with corporations and consumers alike. However the processes that happen behind the scenes to enable these impressive capabilities could make it dangerous for sensitive, government-regulated industries, like insurance, finance, or healthcare, to leverage generative AI without employing considerable caution.

A number of the most illustrative examples of this will be present in the healthcare industry.

Such issues are typically related to the extensive and diverse datasets used to coach Large Language Models (LLMs) – the models that text-based generative AI tools feed off in an effort to perform high-level tasks. Without explicit outside intervention from programmers, these LLMs are likely to scrape data indiscriminately from various sources across the web to expand their knowledge base.

This approach is most appropriate for low-risk consumer-oriented use cases, during which the last word goal is to direct customers to desirable offerings with precision. Increasingly though, large datasets and the muddled pathways by which AI models generate their outputs are obscuring the that hospitals and healthcare providers require to trace and forestall potential inaccuracies.

On this context, explainability refers to the power to grasp any given LLM’s logic pathways. Healthcare professionals trying to adopt assistive generative AI tools should have the means to grasp their models yield results in order that patients and staff are equipped with full transparency throughout various decision-making processes. In other words, in an industry like healthcare, where lives are on the road, the stakes are just too high for professionals to misinterpret the info used to coach their AI tools.

Thankfully, there’s a approach to bypass generative AI’s explainability conundrum – it just requires a bit more control and focus.

Mystery and Skepticism

In generative AI, the concept of understanding how an LLM gets from Point A – the input – to Point B – the output – is way more complex than with non-generative algorithms that run along more set patterns.

Generative AI tools make countless connections while traversing from input to output, but to the skin observer, how and why they make any given series of connections stays a mystery. And not using a approach to see the ‘thought process’ that an AI algorithm takes, human operators lack a radical technique of investigating its reasoning and tracing potential inaccuracies.

Moreover, the repeatedly expanding datasets utilized by ML algorithms complicate explainability further. The larger the dataset, the more likely the system is to learn from each relevant and irrelevant information and spew “AI hallucinations” – falsehoods that deviate from external facts and contextual logic, nevertheless convincingly.

Within the healthcare industry, all these flawed outcomes can prompt a flurry of issues, similar to misdiagnoses and incorrect prescriptions. Ethical, legal, and financial consequences aside, such errors could easily harm the fame of the healthcare providers and the medical institutions they represent.

So, despite its potential to reinforce medical interventions, improve communication with patients, and bolster operational efficiency, generative AI in healthcare stays shrouded in skepticism, and rightly so – 55% of clinicians don’t imagine it’s ready for medical use and 58% distrust it altogether. Yet healthcare organizations are pushing ahead, with 98% integrating or planning a generative AI deployment strategy in an try and offset the impact of the sector’s ongoing labor shortage.

Control the Source

The healthcare industry is commonly caught on the back foot in the present consumer climate, which values efficiency and speed over ensuring ironclad safety measures. Recent news surrounding the pitfalls of near limitless data-scraping for training LLMs, resulting in lawsuits for copyright infringement, has brought these issues to the forefront. Some corporations are also facing claims that residents’ personal data was mined to coach these language models, potentially violating privacy laws.

AI developers for highly regulated industries should subsequently exercise control over data sources to limit potential mistakes. That’s, prioritize extracting data from trusted, industry-vetted sources versus scraping external web pages haphazardly and without expressed permission. For the healthcare industry, this implies limiting data inputs to FAQ pages, CSV files, and medical databases – amongst other internal sources.

If this sounds somewhat limiting, try trying to find a service on a big health system’s website. US healthcare organizations publish tons of if not hundreds of informational pages on their platforms; most are buried so deeply that patients can never actually access them. Generative AI solutions based on internal data can deliver this information to patients conveniently and seamlessly. This can be a win-win for all sides, because the health system finally sees ROI from this content, and the patients can find the services they need immediately and effortlessly.

What’s Next for Generative AI in Regulated Industries?

The healthcare industry stands to learn from generative AI in various ways.

Consider, as an illustration, the widespread burnout afflicting the US healthcare sector of late – near 50% of the workforce is projected to quit by 2025. Generative AI-powered chatbots could help alleviate much of the workload and preserve overextended patient access teams.

On the patient side, generative AI has the potential to enhance healthcare providers’ call center services. AI automation has the ability to deal with a broad range of inquiries through various contact channels, including FAQs, IT issues, pharmaceutical refills and physician referrals. Except for the frustration that comes with waiting on hold, only around half of US patients successfully resolve their issues on their first call leading to high abandonment rates and impaired access to care. The resultant low customer satisfaction creates further pressure for the industry to act.

For the industry to actually profit from generative AI implementation, healthcare providers must facilitate intentional restructuring of the info their LLMs access.