Artificial Intelligence Development in Healthcare in 2022
The 2022 Healthcare AI Survey surveyed more than 300 respondents from around the world to better understand the factors that define healthcare AI. Challenges, achievements and use cases. The results did not change significantly in the second year of the survey, but there were some interesting trends that foreshadowed developments in the coming years. While some aspects of this evolution are positive (democratization of AI), other aspects pose risks (larger attack surface). Here are three trends businesses need to be aware of.
1. Ease of use and democratization of artificial intelligence using no-code tools
According to research firm Gartner, 70% of applications developed by enterprises will use No-code or low-code technologies (less than 25% in 2020). While low-code can simplify programmers’ workloads, no-code solutions that don’t require data science intervention will have a greater impact on enterprises and beyond.
For the healthcare industry, this means that more than half (61%) of Healthcare AI 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, suggests there will be wider industry adoption.
This is important, putting code into the hands of health care workers, just as common office tools like Excel or Photoshop will improve artificial intelligence. In addition to making the technology easier to use, it also enables more accurate and reliable results because medical professionals are now in charge, rather than software professionals. These changes won’t happen overnight, but the increase in domain experts as primary users of AI is a big step forward.
2. Tools are getting more sophisticated and texts are getting more useful
Among 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%), business intelligence (44%), natural language processing (43%) and data annotation (38% ). Text is now the data type most likely to be used in AI applications, and the emphasis on natural language processing (NLP) and data annotation indicates that more sophisticated AI techniques are on the rise.
These tools support important activities such as clinical decision support, drug discovery, and health care policy evaluation. After two years of the pandemic, key progress in these areas is evident as research institutions work to develop new vaccines and uncover the needs of how to better support health care systems in the aftermath of large-scale events. Through these examples, it’s clear that healthcare’s use of AI is very different from other industries and requires a different approach.
So it’s not surprising that both technology leaders and respondents cited the availability of healthcare-specific models and algorithms as the most important requirement when evaluating on-premises installed software libraries or SaaS solutions. Healthcare-specific models will grow in the coming years, judging by the venture capital landscape and demand from AI users.
3. Increased security concerns
With all the progress made in artificial intelligence over the past year, it has also opened up a range of new attack vectors. When respondents were asked what types of software they use to build their AI applications, the most popular choices were locally installed commercial software (37%) and open source software (35%). Most notably, usage of cloud computing services fell 12% (30%) compared to last year's survey, most likely due to privacy concerns about 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. 68% of respondents expressed a clear preference for using internal assessments and self-tuning models. Likewise, with the strict controls and procedures surrounding healthcare data processing, it is evident 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 also face challenges, the consequences of a data breach in healthcare 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 get the technology into the hands of users. As AI becomes more widely available, and as models and tools improve, safety and ethics will become important areas of concern. It will be important to understand how artificial intelligence in healthcare develops this year and what this means for the future of the industry.
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