Welcome to Digital Acceleration News. By popular demand, we will comment on recent news related to Digital Transformation: AI, ML, IoT, Cybersecurity, success stories, and Systems Integrators Whitepapers. Click on Subscribe to be notified of new episodes.

I have a couple of requests from subscribers asking for industry segment news.

This week I will comment on healthcare-related news.

Note: All Illustrations were created by Dreamstudio.ai, an AI art-generator service

I. Artificial Intelligence and Radiology

This week, HealthImaging is publishing an article where the main topic is the need to standardize the integration of the radiology AI engines with the Enterprise Medical footprint and why many AI providers are focused on connecting their systems to a major Electronic Medical Record (EMR), or Picture Archiving and Communication System (PACS).

Why is this important?

The first time I heard about Radiology uses of AI was during an interview with one of the leaders of IBM Watson, maybe back in 2015. During that interview, the primary concern was how to analyze an image that was not taken using the correct procedure: little positioning and angle misalignments during the image capture.

Since then, it seems that the quality of the training algorithm has matured, and the new challenge to solve is the integration piece: Governance, Patient Privacy, and ease of use. That is why a significant effort is connecting these new medical applications to the core Medical Infrastructure.  (EMR, PACS).

II. US Government Agency published a document with the benefits and challenges of AI in Healthcare

US GAO published a fascinating document about the benefits and challenges of Machine Learning technologies for Medical Diagnostics.

Why is this important?

First, it is essential to recognize the importance of quality data feed ML solutions. Recently, there has been a recent discussion about two topics: (1) Biased information and (2) Completeness of Data Samples to train the AI engine. GAO highlights the importance of having quality data to train AI-based solutions.

This move made by the US GAO (Government Accounting Office) is an important milestone:

1. It shows a crucial interest in exploring better ways to provide healthcare to the citizens,
2. Shows commitment by documenting current best practices, lessons learned, and existing limitations of AI, ML, and Training Data, assisting physicians in reviewing cases and confirming diagnostics.

Special mention to specific use cases analyzing medical cases such as Cancers, Alzheimer’s disease, diabetic retinopathy, and COVID.

This is an important milestone, more to come.

News bonus:

III.Top 10 uses of Machine Learning in Image Analysis in the Healthcare Industry

Analytics Insight published an interesting article with ten use cases with a lot of interest and success stories, from disease and diagnostics identification, drug development, clinical research and trials, and smart health record, among others.

Must read.

Why is this news important?

As we live our daily lives, we lose focus on significant advantages in industry segments, such as Healthcare.

Even though there is much concern related to the responsible and ethical use of AI and ML, these improvements are going in the right direction.

Good enough?

I hope you find this Episode valuable and entertaining

What are your thoughts on the subjects raised in this edition of the Digital Acceleration Newsletter?

You are welcome and encouraged to share your thoughts with the audience.