Understanding options for solving these challenges

While there are known challenges, there are ways organizations can successfully adopt ML in healthcare and life sciences workloads. One important factor to consider is at what stage in the workflow you apply ML. While there have been improvements to the technology, it is not a replacement for a trained medical professional. It cannot replace the practical experience and knowledge gathered through years of practice and training. However, technology can help make medical professionals more efficient at what they do. For example, instead of relying on an ML model to make diagnostic decisions for a patient, it can be used to recommend a diagnosis to a healthcare provider and let them make a final decision. It can act as a tool for them to search through a variety of case reports and medical history information that will help them in their decision-making process. The medical professional is still in control while getting a superpower that...