Barriers to AI in Healthcare: Physician Acceptance and Comfort

Artificial intelligence gets a lot of buzz as a leading-edge technology in healthcare. But it still has a long way to go when it comes to adoption, with only 20% of physicians saying AI has changed the way they practice medicine, according to a recent survey.   In fact, the majority of physicians are anxious or uncomfortable with […]

Barriers to AI Adoption in Healthcare: Burden of Evidence

For AI-based solutions to become part of the daily practice of medicine, A myriad of technical, economic, regulatory, and other forms of barriers exist. Many of these have yet to be addressed sufficiently so the applications of AI in Medicine can truly take off. So, even after many of the data issues we’ve discussed here […]

Model Bias: Part III

No data set can represent the entire universe of options. Thus, it is important to identify the target application and audience upfront, and then tailor the training data to that target. Another possible approach could be  to train multiple versions of the algorithm, each of which is trained to input a dataset and classify it, […]

Model Bias: Part II

On another front, AI algorithms are designed to learn patterns in data and match them to an output. There are many AI algorithms, and each has strengths and weaknesses. Deep learning is acknowledged as one of the most powerful today, yet it performs best on large data sets that are well labeled for the precise […]

Model Bias: Part I

Bias in AI occurs when results cannot be generalized widely. Although most people associate algorithm bias resulting from preferences or exclusions in training data, bias can also be introduced by how data is obtained, how algorithms are designed, and how AI outputs are interpreted.   This issue touches on concerns that are also more social […]

Can You Take Your Model With You?

One of the key issues with AI is that algorithms developed in one institution or one set of data may not perform as well when used at different institutions with different data. Researchers at Mount Sinai’s Icahn School of Medicine found that the same deep learning algorithms diagnosing pneumonia in their own chest x-rays did […]

AI Model Transparency

Besides issues in getting a hold of large and diverse datasets, annotation or labeling, and sexy new methods to train models (synthetic data and federated learning!,) transparency also relates to model interpretability—in other words, humans should be able to understand or interpret how a given technology reached a certain decision or prediction. AI technologies will […]

Synthetic Data

We have been examining the issues of obtaining data (enough of it! and high quality) and preparing that data to be used in training and validating models. One emerging way to deal with the issue of creating datasets for algorithm training is by creating synthetic data. This builds on the idea of using some of […]

Data Labeling and Transparency

Transparency of data and AI algorithms is also a major concern. Transparency is relevant at multiple levels. First, in the case of supervised learning, , the accuracy of predictions relies heavily on the accuracy of the underlying annotations inputted into the algorithm. Poorly labeled data will yield poor results so transparency of labeling such that […]

Federated AI As A Solution? Part III

In the final installment in this series about federated AI, we wrap up the discussion around the potential benefits of federated AI in developing good models in healthcare. Remember that this extensive discussion of federated AI was preceded by our examination of the many obstacles that exist in healthcare in getting enough good data to […]