Healthcare AI: Three trends to watch
Between the COVID-19 pandemic, the mental health crisis, rising healthcare costs and an aging population, industry leaders are rushing to develop artificial intelligence (AI) for healthcare application. One sign from the venture capital market is that more than 40 startups have raised $20 million or more to build artificial intelligence solutions for industries. But how is artificial intelligence actually used in healthcare?
The 2022 Healthcare AI Survey surveyed more than 300 respondents from around the world to better understand what defines healthcare Challenges, achievements, and use cases of AI. This is the second time the survey has been conducted, and the results don’t change significantly, but they do point to some interesting trends that bode well for how medical AI will develop in the coming years. While some aspects of this evolution are positive (the democratization of AI), other aspects are less exciting (the existence of a larger attack surface).
Here are three trends that enterprises need to understand:
1. Ease of use and democratization of artificial intelligence using no-code tools
Gartner estimates that by 2025 , 70% of new applications developed by enterprises will use no-code or low-code technology (less than 25% in 2020). While low-code simplifies programmer workloads, no-code solutions that don’t require data science intervention will have the greatest impact on enterprises and other sectors.
That’s why it’s exciting to see a clear shift in the use of AI from technical titles to domain experts themselves.
For the healthcare industry, this means that more than half (61%) of respondents to the AI in Healthcare survey identified clinicians as its target users, followed by healthcare payers (45%) and healthcare IT companies (38%). Coupled with significant development and investment in AI applications for healthcare and the availability of open source technology, these bode well for broader industry adoption.
For healthcare, this means more than half (61%) of AI in Healthcare survey respondents identified clinicians as their target users, followed by healthcare payers (45%) and healthcare IT companies (38%). This, combined with significant development and investment in healthcare-specific AI applications and the availability of open source technology, points to broader industry adoption.
This is significant: Putting code in the hands of healthcare workers, like common office tools like Excel or Photoshop, will improve AI. In addition to making the technology more accessible, it also makes the results more accurate and reliable because medical professionals, not software professionals, are now in the driver's seat. These changes didn’t happen overnight, but the increase in domain experts as primary users of AI is a big step.
2. Tools are getting more sophisticated and texts are becoming more useful
Other encouraging findings involve advances in AI tools and users’ desire to delve deeper into specific models. When asked what technologies they plan to adopt by the end of 2022, technology leaders in the survey cited data integration (46%), BI (44%), NLP (43%) and data annotation (38%). Text is now the data type most likely to be used in AI applications, and an emphasis on natural language processing (NLP) and data annotation suggests that more sophisticated AI techniques are on the rise.
These tools support important activities such as clinical decision support, drug discovery, and healthcare policy evaluation. Two years into the pandemic, it’s clear how important progress in these areas is as we develop new vaccines and discover how to better support healthcare system needs in the aftermath of large-scale events. Through these examples, it is also clear that the use of AI in the medical industry is very different from other industries and requires a different approach.
So it’s no wonder that both technology leaders and interviewees from mature organizations cited the availability of healthcare-specific models and algorithms as the most important need when evaluating on-premises installed software libraries or SaaS solutions. Strange. Judging from the venture capital landscape, existing information in the market, and demand from AI users, healthcare-specific models will only grow in the coming years.
3. Security concerns are growing
With all the progress artificial intelligence has made in the past year, it has also opened up a series of new attack vectors. When asked what types of software respondents use to build their AI applications, the most popular choices are locally installed commercial software (37%) and open source software (35%). Most notably, usage of cloud services fell
by 12% (30%) compared to last year's survey, most likely due to privacy concerns over data sharing.
Additionally, the majority of respondents (53%) choose to rely on their own data to validate models, rather than third-party or software vendor metrics. Respondents from mature organizations (68%) have a clear preference for using internal assessments and adapting models themselves. Additionally, there are strict controls and procedures in place regarding the processing of medical data, and it is clear that AI users will want to keep operations in-house where possible.
But regardless of software preferences or how users validate models, escalating healthcare security threats can have a significant impact. While other critical infrastructure services face challenges, the consequences of a healthcare breach extend beyond reputational and financial damage. Data loss or tampering with hospital equipment can be the difference between life and death.
Artificial intelligence is poised for even more significant growth as developers and investors work to put the technology into the hands of everyday users. But as AI becomes more widely available, and as models and tools improve, safety, reliability, and ethics will become important areas of focus. It will be interesting to see how AI in these healthcare areas develops this year and what this means for the future of the industry.
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