This is an excerpt of the article “Why is AI adoption in health care lagging?”, with a focus on the topic above.
Brookings
Avi Goldfarb and Florenta Teodoridis
March 9, 2022
Excerpt
by Joaquim Cardoso MSc.
The AI Health Care Unit
@ The Digital Health Institute
March 13, 2022
The opportunity for AI in Health Care
- In 2019, 11% of American workers were employed in health care, and health care expenditures accounted for over 17% of gross domestic product.
- If AI technologies have a similar impact on healthcare as in other industries such as retail and financial services, then health care can become more effective and more efficient, improving the daily lives of millions of people.
- However, despite the hype and potential, there has been little AI adoption in health care.
Four important barriers to adoption are:
- algorithmic limitations,
- data access limitations,
- regulatory barriers, and
- misaligned incentives
Two important barriers to adoption, from a technical nature are:
- algorithmic limitations,
- data access limitations
1.Algorithmic limitations
- Advances in neural networks pushed forward the possibility boundaries of AI at the cost of interpretability.
- When neural networks are used, it is often difficult to understand how a specific prediction was generated, meaning without substantial effort, some AI algorithms are so-called “black boxes.”
- This lack of transparency can reduce trust in AI and reduce adoption by health care providers, especially considering that doctors and hospitals will likely be held accountable for decisions that involve AI.
- There are several large-scale initiatives that focus on developing and promoting trustworthy AI. 15
- Interpretable AI might increase trust by eliminating the black box problem, allowing health care workers to understand how AI reaches a certain recommendation.
- Others are innovating in developing clinical trial standards for AI systems. 16
2.Data access limitations
- Medical data is often difficult to collect and difficult to access.
- Electronic Healthcare Record (EHR) systems are largely not compatible across government-certified providers that service different hospitals and health care facilities.
- It is also difficult to pool such data across hospitals or across health care providers … data collection is localized rather than integrated to document a patient’s medical history across his health care providers.
- Without large, high-quality data sets, it can be difficult to build useful AIs. This, in turn, means that health care providers may be slower to take up the technology
Transcription of the text excerpted from the original publication, and summarized above.
1.Algorithmic limitations
Advances in neural networks pushed forward the possibility boundaries of AI at the cost of interpretability.
When neural networks are used, it is often difficult to understand how a specific prediction was generated, meaning without substantial effort, some AI algorithms are so-called “black boxes.”
When neural networks are used, it is often difficult to understand how a specific prediction was generated, meaning without substantial effort, some AI algorithms are so-called “black boxes.”
As a result, if there is no one proactively looking to identify problems with a neural network-generated algorithm, there is a substantial risk that the AI will generate solutions with flaws only discoverable after they have been deployed — for examples, see work on “algorithmic bias.” 14
This lack of transparency can reduce trust in AI and reduce adoption by health care providers, especially considering that doctors and hospitals will likely be held accountable for decisions that involve AI.
This lack of transparency can reduce trust in AI and reduce adoption by health care providers, especially considering that doctors and hospitals will likely be held accountable for decisions that involve AI.
The importance of complementary innovation in trustworthy AI, for example through technologies or processes that facilitate AI algorithm interpretation, is widely recognized.
There are several large-scale initiatives that focus on developing and promoting trustworthy AI. 15
Interpretable AI might increase trust by eliminating the black box problem, allowing health care workers to understand how AI reaches a certain recommendation.
Interpretable AI might increase trust by eliminating the black box problem, allowing health care workers to understand how AI reaches a certain recommendation.
Others are innovating in developing clinical trial standards for AI systems. 16
These innovations are likely to facilitate the adoption of AI in health care because it would allow health care professionals to better understand the likelihood that an AI reached its recommendation in a biased or incomplete manner.
Others are innovating in developing clinical trial standards for AI systems.
2.Data access limitations
The performance of AI algorithms is also contingent on the quality of data available. Thus a second barrier to adoption is limited access to data.
Medical data is often difficult to collect and difficult to access. Medical professionals often resent the data collection process when it interrupts their workflow, and the collected data is often incomplete. 17
Medical data is often difficult to collect and difficult to access.
It is also difficult to pool such data across hospitals or across health care providers.
Electronic Healthcare Record (EHR) systems are largely not compatible across government-certified providers that service different hospitals and health care facilities. 18
The result is data collection that is localized rather than integrated to document a patient’s medical history across his health care providers.
Without large, high-quality data sets, it can be difficult to build useful AIs. This, in turn, means that health care providers may be slower to take up the technology.
It is also difficult to pool such data across hospitals or across health care providers … data collection is localized rather than integrated to document a patient’s medical history across his health care providers.
Without large, high-quality data sets, it can be difficult to build useful AIs.This, in turn, means that health care providers may be slower to take up the technology.
About the authors
Avi Goldfarb is a consultant with Goldfarb Analytics Corporation, which advises organizations on digital and AI strategy.
Florenta Teodoridis, Assistant Professor of Management and Organization — USC Marshall School of Business
The authors did not receive financial support from any firm or person for this article or from any firm or person with a financial or political interest in this article. Other than the aforementioned, the authors are not currently an officer, director, or board member of any organization with a financial or political interest in this article.
Originally published at https://www.brookings.edu on March 9, 2022.
