A new survey conducted by GE Healthcare has revealed that there is a significant level of distrust and skepticism surrounding the use of Artificial Intelligence (AI) in medical settings. The Reimagining Better Health poll surveyed 5,500 patients and patient advocates and 2,000 clinicians, and found that while the majority of doctors believe that AI has the potential to transform healthcare, many feel that the technology is not yet ready and remains marred by roadblocks such as biases.
Despite this, many healthcare giants are exploring the use of AI models, including generative technologies like ChatGPT and conversational AI, to improve patient experience and outcomes, automate tasks and enhance productivity.
Clinicians believe that AI can help with decision-making, enable faster health interventions, and help improve operational efficiencies. However, some remain concerned about the risks associated with the adoption of AI in the field. Specifically, 55% of survey respondents said that AI technology is not yet ready for medical use and 58% implied that they do not trust AI data. Clinicians with more than 16 years of experience expressed even higher levels of skepticism, with 67% lacking trust in AI.
The biggest reason for this distrust is the potential for algorithms to produce unfair or discriminatory outcomes due to various factors such as incomplete training data, flawed algorithms, or inadequate evaluation processes. As many as 44% of the respondents said that the technology is subject to built-in biases.
Clinician awareness of the technologies involved is not always up to mark, as only 55% of surveyed clinicians feel they get adequate training on how to use medical technology.
GE Healthcare CTO Taha Kass-Hout suggests that a thoughtful, data-driven approach is the key to building confidence among clinicians who are on the fence about AI technology. Kass-Hout explains that the company pays special attention to where data sets come from and the characteristics of the population sampled. They also evaluate the algorithms that classify and organize data and look at the AI formulation itself and clinicians’ feedback when updating these algorithms.
To build clinician understanding of where and how to use AI, companies should drive training/education programs where clinicians are guided on all things AI, from how it works to how it can augment their work. Kass-Hout refers to this as “breaking the black box of AI” to help clinicians understand what is in the AI model. This includes what data it comprises, such as age, gender, lab results, remote monitoring, medical history, genetic variant or biomarker, lesion progression in subsequent images, so clinicians can better understand what is influencing the AI output.
“Transparency on what influences the model and how it can be adjusted with a consistent feedback loop over time is critical to building confidence in AI technology among clinicians,” he noted.
As healthcare systems around the world face extreme pressures, clinicians are burning out and considering leaving the industry. According to the World Health Organization, there could be a shortage of 10 million health workers by 2030, when 1.4 billion people will be 60 or more. In such scenarios, AI-driven systems could come in and eliminate repetitive low-level tasks to help workers focus solely on patients’ care.
GE HealthCare’s Command Center is an example of this. The platform is helping hospitals use real-time utilization data to better allocate resources. “Using AI technology, hospitals can redirect ambulatory services to bring patients to facilities with lower utilization — helping to reduce burnout,” Kass-Hout said.
In another example, Hyro, a company providing plug-and-play conversational AI assistants for the healthcare industry, is automating tasks like patient registration, routing, scheduling, IT helpdesk ticketing, and prescription refills, which constitute roughly 60-70% of inbound calls and messages into health systems.
“While we are still in the early stages of seeing the true impact of these technologies, with appropriate human supervision, AI can help to reduce the burden of data query and analysis on clinicians so that they can be focused on what really matters: Improving patient outcomes,” Kass-Hout noted.
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