The Informatics e-Learning Hub: Cyber Security

Ransomware in Healthcare

By: Hope Palmer

Ransomware is special software designed to encrypt files and computer systems, making them inaccessible until a ransom is paid. Unlike data theft and traditional viruses, ransomware creates urgency to pay by disabling business operations. These attacks can shut down entire hospital networks and create harm to patients and liability to practices.

This video provides an overview of what ransomware is, how hackers gain access, and how this type of malware can impact radiology.

In this video, radiologists will learn:

What ransomware is.
About phishing strategies.
About real-life examples of ransomware.
Best practices for avoiding cyberattacks.

Lessons Learned: Ransomware Recovery

By: Howard Chen, MD, MBA

Recognizing that the question is not “whether” but “when” a disabling ransomware attack may cause operational disruption, attention toward operational recovery becomes increasingly important. Operational recovery from a digital cyberattack can be ironically analog, relying heavily on pen, paper, and optic discs for data storage, and human runners and fax machines for data transfer.

This video provides an overview of ransomware as a business continuity/disaster recovery challenge and planning considerations unique to radiology.

In this video, radiologists will learn:

About the impact of ransomware on imaging operations.
Best practices to reduce the impact on imaging operations.
Recovery considerations at each phase of a disabling attack.

Assess-AI and AI-PROBE: Monitoring Algorithm Performance in Clinical Practice

By: Axel Wismüller, MD, MSc, PhD

Assess-AI provides monitoring of algorithm performance in clinical practice by capturing real-world data during clinical use in a clinical data registry. This video introduces AI-PROBE (Artificial Intelligence Prospective Randomized Observer Blinding Environment) and how it can contribute additional study-specific information such as turnaround times, application specific outcome measures, and cost-effectiveness measures to the existing Assess-AI data repository.

In this video, radiologists will learn:

How Assess-AI works in post-market surveillance.
An overview of AI-PROBE and how it is used for performance evaluation.
How AI-PROBE can augment data repositories, such as Assess-AI.

Bias and Fairness in AI Models

By: Monica J. Wood, MD

Unwanted bias may be incorporated unwittingly into AI models at points throughout an algorithm’s lifecycle — including the creation of training datasets, selection of model architecture and refinement of the algorithm post-deployment. And just as radiologists perform quality assurance on all our imaging modalities, we should aim to incorporate bias evaluation in routine assessment of AI algorithms.

Learning Objectives

How biases make their way into AI algorithms.
Examples of bias affecting the training data.
How to engage in meaningful conversations on bias.

Brittleness of AI Models

By: Woojin Kim, MD

“Brittleness” is the term for an AI model which works well where it was trained but fails when taken outside that area and subjected to new data. There are no guarantees that an algorithm developed elsewhere will function well at your institution — even if it has FDA-clearance. The video explores issues relating to brittleness including concept drift, where the goal of the algorithm changes over time, and data drift, which involves changes created by using new data sources to generate medical imaging data.

Learning Objectives:

The limitations of AI and what can be done to mitigate them.
How validation and training issues contribute to brittleness.
What radiologists should look for in evaluating an AI algorithm.