Do No Harm: Machine Learning Models as a Decision-Making Tool in Healthcare
While artificial Intelligence (AI) can take many forms, machine learning is one area that has received a lot of attention.
In the healthcare space, the machine learning approach has been leveraged with clinical decision-making. Existing data is utilized to “train” decision-making algorithms that are leveraged to help healthcare professionals make diagnostic or treatment decisions.
The power of these tools to make difficult diagnostic decisions is impressive. But what role should AI play in clinical decision-making, both now and moving forward?
In this whitepaper, Richard Durante and Andrew Grenville of Maru/Matchbox look at why AI-driven tools should be just one of many inputs into the decision making process.
At Maru/Matchbox, our life sciences and healthcare team is a group of senior consultants who have an unyielding drive to unearth and deliver insights capturing the impact of these macro-level changes on the development, marketing, and delivery of healthcare-related products and services. To do this, we listen. We leverage a toolkit of innovative methodologies and approaches for finding the right individuals, asking the right questions, and carefully listening to what they tell us. By collecting and analyzing data in this way from all healthcare stakeholders and combining it with a deep understanding of healthcare and the ability to tell a compelling story, we generate insights that empower our clients to react to and anticipate market changes and to make sound marketing decisions.