The use of AI in healthcare has been steadily increasing, a development that is both hopeful and worrisome if left unchecked.
Artificial intelligence technology has made remarkable progress in the past decade. Computers can accurately classify images and map the environment, giving cars, drones and robots the ability to navigate real-world spaces. Artificial intelligence enables human-computer interaction that was not possible before.
Because of this, AI is being explored for a wide range of healthcare applications. This includes improving patient care, accelerating drug discovery, and enabling efficient operation and management of the healthcare system.
The primary goals of patient care include the analysis of radiological images and tissue samples for detection and diagnosis, as well as personalized precision medicine for disease treatment and treatment. But caution is especially important when machines are in the midst of making life-or-death decisions.
The focus should be on artificial intelligence that can assist human decision-making in healthcare settings rather than replace it. A framework of humans working with machines to make such decisions is well worth pursuing, as it recognizes that machines can provide key insights that complement medical professionals.
It is also worth considering that the machine’s judgment can be potentially seriously flawed. Depending on the AI tools used, they may also lack the ability to explain the reasons for a particular decision in a way that patients and doctors can trust.
Factors Affecting the Credibility of AI Healthcare Decisions
There are many factors that affect the trustworthiness of an AI system. Bias has been widely recognized as a major problem in AI-based decision-making systems.
A sort of blog Michael Jordan, a professor of computer science and statistics at the University of California, Berkeley, highlighted the story of his pregnant wife being told she was at increased risk of giving birth to a child with Down syndrome. Their ultrasound showed white spots around the baby’s heart, an indicator of the condition. However, this result is based on a statistical model using a much lower resolution imager. In this case, the increased resolution and increased noise in the measurement led to the recommendation to perform a risky amniocentesis. Fortunately, they decided not to proceed with the operation, and Jordan’s wife gave birth to a healthy baby a few months later. Others may not be so lucky.
Such experiences underscore the need for a principled approach in building and validating AI-based decision-making systems. In addition to data quality, bias and robustness issues, there is a need to develop interpretable and explainable systems and risk management strategies to prioritize and make decisions. Having good frameworks and policies will help AI systems make better decisions and build trust among stakeholders.
Other factors involve moral and social issues. This is important for any AI-based decision-making system, but also critical for systems responsible for ensuring safety. We can imagine a healthcare management system that decides which patients should receive treatment with a limited supply, or be admitted to the ICU before others who need more urgent care.
There are concerns about privacy and the expectation that AI systems will have some level of transparency and accountability. Some of these questions do not have clear answers and require further thought.
Rescue certification?
Many industries benefit from supporting standards that provide some level of guidance around product or service development, production, and distribution. The International Organization for Standardization (ISO) has established a number of management system standards that set requirements that help organizations manage their policies and processes to achieve specific goals.
The AI community is developing a set of standards to guide industry best practices. Methods have been created to assess the robustness of neural networks and the bias of artificial intelligence systems. Other projects in development will specify risk management processes, ways to deal with harmful biases, and ways to ensure transparency. Healthcare systems will definitely have stricter requirements in terms of data quality, reporting requirements, etc. compared to other industries.
While standards and certification programs won’t be a panacea, they will ultimately provide a framework for responsible use of AI, measuring the effectiveness and efficiency of their systems, managing risk, and continuously improving processes. It’s still a few years away, but the community is working toward that goal.
Assist in the decision-making process
So what can we do in the meantime? We should focus on artificial intelligence that can assist the decision-making process, including tools that can help medical professionals make informed decisions.
Systems that can handle or assist with routine tasks such as patient registration, obtaining vital signs, and maintaining patient records are also beneficial. They help medical professionals spend more time on urgent issues and create opportunities for more face-to-face interactions with patients.
For example, imagine a technology solution that enables non-contact line-of-sight monitoring of vital signs such as heart rate, breathing rate, and body temperature in places where people congregate. Installing such camera systems in nursing homes or residences where seniors are “aging in place” could continuously monitor their condition and could alert caregivers or medical professionals to changes in personal health that may require attention.
As technology develops and our understanding of AI-based decision-making processes improves, we certainly expect it to play a greater role in healthcare decision-making.
according to American Hospital Association, the U.S. will face a shortage of 124,000 doctors by 2033 and will need to hire at least 200,000 nurses each year to meet the growing demand.This American Health Care Association and National Center for Assisted Living (AHCA/NCAL) It was also found that 99 percent of nursing homes and 96 percent of assisted living facilities are facing staffing shortages.
Given these sobering numbers, the growth of AI and automation of healthcare applications will be critical in the coming decades. It highlights the need for artificial intelligence to help healthcare professionals today and tomorrow work more efficiently and smarter without sacrificing safety.
While still a few years away, AI-driven solutions will align with emerging industry standards to provide tools to safely assess and monitor those in need of care, assist patients in diagnosing and recommending treatments, and significantly improve the quality of patient care.
Photo: metamorworks, Getty Images
