AI in Healthcare Blogs

According to a survey of over 12,000 participants conducted by the consultancy PwC, lack of trust and a need for the human element were the biggest hurdles to using AI in healthcare.  Another survey by KPMG in 2020 revealed a number of … Read More

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 … Read More

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 … Read More

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 … Read More

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, … Read More

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 … Read More

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 … Read More

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 … Read More

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 … Read More

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 … Read More