Poor data is hindering machine learning, US drug development, study says: A lack of proper data is hurting the use of machine learning to develop drugs, which could put U.S. drugmakers at a competitive disadvantage compared to other countries, according to a report from the U.S. Government Accountability Office and the National Academy of Medicine.

A lack of proper data is hurting the use of machine learning to develop drugs, which could put U.S. drugmakers at a competitive disadvantage compared to other countries, according to a report from the U.S. Government Accountability Office and the National Academy of Medicine. Machine learning is a type of artificial intelligence that involves using data to train computers to make decisions and learn from experiences, according to Pharmaphorum. It has the potential to cut costs of research and development for drugmakers by helping researchers to predict what will and won't work in clinical trials. However, the report says a lot of the data being used in drug development is not suitable for machine learning purposes. There is a phenomenon known as "garbage in, garbage out," where a machine learning system can't produce credible results because of poor data, according to Pharmaphorum.