AI Need in Healthcare
Artificial intelligence, also known as AI, has become an essential part of the health sector. It aids doctors in making crucial decisions, like how to prepare cancer patients for hard choices. At the University of Pennsylvania Health System, AI nudged doctors to discuss treatment options and end-of-life preferences with their patients. But the path of AI in healthcare isn’t always smooth sailing.
AI’s Performance During the Pandemic
During the Covid-19 pandemic, AI system’s performance took a hit. According to a 2022 study, the AI tool’s prediction accuracy dropped approximately 7%. This predicament forced doctors to miss crucial conversations with patients who needed them, impacting their overall treatment plan. This is not an isolated case; many AI tools designed to optimize medical care saw a dip in their performance during the pandemic.
Growing Need for Constant Monitoring
The recurring issues with AI systems highlight the need for constant monitoring and evaluation in their deployment. Any malfunction or drop in performance can alter the outcome significantly, impacting patient care. Constant performance monitoring and regular updates can help keep these tools on track.
The Cost of AI Maintenance
While AI can increase accessibility and improve care, it can also elevate the cost. Adding more resources for monitoring and validating AI tools can add up significantly and become financially untenable. Decision-makers at hospitals have to walk a tightrope between improved care and cost management.
AI’s Pervasive Presence in Healthcare
Artificial Intelligence tools have become indispensable in healthcare, with multiple uses such as predicting death risk, suggesting diagnoses, recording and summarizing visits, and processing insurance claims. Given the extensive uses and the potential for errors, the need for a standardized system to evaluate the AI’s performance becomes even more important.
Selecting the Right AI Tool
Hospitals and care providers face the challenge of selecting the best AI algorithm according to their specific needs. Current literature and sources do not offer substantial guidance, making this decision-making process even more difficult.
AI Documentation: A Problem of Standards
An example of the growing AI use in healthcare is ambient documentation. By listening to and summarizing patient visits, tech-enabled assistants are now commonplace in doctors’ offices. Yet without standardized models for comparing their output, the risk of errors increases – a risk that can have devastating consequences in the field of healthcare.
Potential Issues with AI Implementation
In some cases, the reason for the failure of AI algorithms can be traced back to data changes, while in other cases, the issues seem to appear without a clear cause. In such situations, the faulty predictions of AI can significantly impede the effectiveness of medical care.
The High Cost of AI Monitoring
Monitoring and maintaining AI systems in healthcare is a significant responsibility, requiring considerable resources and financial investment. For instance, auditing just two models for fairness and reliability took around 8 to 10 months at Stanford, with approximately 115 man-hours dedicated to the task.
Future of AI in Healthcare
Many experts suggest that the future of healthcare might hinge on AI monitoring AI, under human supervision. But such a system implies more spending and resources, posing additional challenges considering the realities of hospital budgets and the limited supply of AI tech specialists.
Final Thoughts
The presence of AI in healthcare shows promise, yet it’s not without its hiccups. Continuous efforts in monitoring, evaluation and improvements in the field can pave the way for meaningful use of AI in the patient care process.