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Ensuring AI Success in Healthcare: The Vital Role of ML Monitoring

Artificial intelligence (AI) is revolutionizing healthcare, with significant advancements in disease diagnosis, patient outcome predictions, and overall patient care and safety. According to the Future Health Index 2023 report by Philips, 83% of healthcare leaders plan to invest in AI in the next three years, up from 74% in 2021.

As you can see in the graph below, there continues to be an investment in AI for optimizing efficiency and integrating diagnostics, but going into 2023, there is more reliance on AI to help predict patient outcomes and support clinical decisions.

Future Health Index 2023 report by Philips: How planned investments in AI three years from now have evolved between 2021 and 2023, according to healthcare leaders

The importance of ML monitoring in healthcare

With the growing reliance on AI systems, it's important to acknowledge that the likelihood of bad or changing data resulting in incorrect predictions or recommendations also increases. Healthcare companies need to keep an eye on the well-being of their models and the quality of the data used to train and update them to ensure their AI systems are performing as expected.

Here are some key reasons implementing an ML monitoring solution should be a top priority across the healthcare industry:

Identifying issues before they become serious problems

By proactively monitoring ML models and the underlying data, organizations can catch issues before they become serious problems and avoid making decisions based on degraded models, drifting data, or biased outcomes. This helps ensure the reliability and fairness of ML-driven healthcare business processes.

Improving patient outcomes

AI has the potential to improve patient outcomes significantly through predictive analytics, disease diagnosis and detection, and treatment recommendations. However when errors or biases occur, it can have serious consequences to patient care and safety. By continuously monitoring these models, healthcare organizations can identify potential issues early on, allowing for timely intervention and improvement in patient care.

Reducing costs

By embracing AI systems, healthcare organizations can unlock enormous advantages in cost optimizations for staffing, operations, and supply chain management. However, the data that powers these models degrade over time, resulting in incorrect cost and resource predictions which if left unnoticed, can result in financial losses. ML monitoring can help ensure these applications deliver the expected benefits, allowing you to save costs without sacrificing quality.

Ensuring compliance and regulation

In highly regulated industries like healthcare, AI applications must adhere to a range of regulations and standards. Healthcare providers bear the responsibility of safeguarding patient privacy and ensuring ethical data use, making monitoring essential. It enables providers to identify bias, maintain fairness, track data usage, monitor model performance, and promptly detect potential breaches or misuse of sensitive information.

Key components of ML monitoring in healthcare

Real-time monitoring empowers healthcare companies to quickly pinpoint deviations, anomalies, or ML performance issues. Visibility into model performance allows you to take immediate corrective actions or implement measures to mitigate risks.

Here are some of the key components of ML monitoring in healthcare:

Model performance monitoring

Model performance describes the accuracy of the model's predictions, and how effectively it can perform its tasks with the data it has been trained on. Performance metrics should be tracked to assess how well the model is performing in real-world healthcare scenarios. Without continuous visibility into an ML model’s input and output, you risk model performance degradation caused by data drift, data quality, schema changes, and more.

The WhyLabs platform can help switch on ML monitoring with minimal configurations as well as support entirely custom metrics. Once an issue is detected, a wide range of tools for root cause analysis such as segmentation (slicing & dicing) and tracing (identifying the worst performing segments) help pinpoint the issue in a matter of minutes.

Ensuring quality data

Data quality refers to the consistency and relevancy of a data set. As data pipelines handle larger volumes of data from a variety of sources and increase in complexity, data quality becomes one of the most important factors to overall model health. If you're not careful, poor data quality can cause your pipeline and models to fail in production, which may not only be costly but can also compromise patient care.

With WhyLabs, you can detect data quality issues anywhere in your ML pipeline and set up notifications to receive a summary of data quality anomalies so you can keep tabs on your data health metrics without having to manually check in on them.

Bias detection

Bias refers to a systematic error in a model's predictions or decisions, caused by the model's inability to capture the true underlying relationship between the input variables and the output variable. If bias goes unnoticed, it can lead to inaccuracies or discrimination against certain groups or individuals.

Quickly identify if your model is making predictions based on features that can be introducing bias with purpose-built Bias Tracing in WhyLabs and discover which segments within your data contribute negatively or positively towards your model performance.

Start your ML monitoring journey

Now, more than ever, accuracy, reliability, and safety are top priorities in the world of AI. To achieve these goals and instill confidence in your AI applications, it is essential to implement a robust ML monitoring solution. Take charge of your AI journey and build trust among stakeholders with an effective monitoring system.

WhyLabs offers a privacy-preserving architecture that does not involve data duplication, making it an ideal solution for ML applications in highly regulated industries like healthcare. Major healthcare organizations rely on WhyLabs to power AI system monitoring to ensure ML models are accurate, compliant and meet patient safety standards.

Sign up for a free WhyLabs account or schedule a demo to see how the WhyLabs AI Observatory platform is enabling healthcare organizations to:

  • Get real-time insights into all AI-powered decisions
  • Ensure patient safety and improve the quality of care
  • Catch and fix issues before they impact the business or patient care
  • Monitor AI applications without compromising data privacy

Don’t just take our word for it - read how a major healthcare provider met Model Health Equity Governance guidelines and minimized time-to-insight across model operation tasks with WhyLabs!


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