AI in Healthcare is Powerful but Skepticism Abounds
Artificial Intelligence (AI) is delivering tremendous value in Healthcare. For example, recent news out of GoogleHealth and DeepMind, as reported in the journal Nature (“International evaluation of an AI system for breast cancer screening”) details how their AI solution potentially spots cancer earlier and more accurately than radiologists in the United States and United Kingdom. But skepticism abounds.
Promising as GoogleHealth/DeepMind’s AI-based breast cancer detection solution is, there are significant “trust issues” including a lack of transparency with study data and models, the black box nature of many AI solutions, potential biases, and a lack of explainability. More serious trust issues obviously relate to model performance and their ability to deliver proper diagnoses or outperform clinicians. Risks include unnecessary procedures and the possibility of misdiagnosis, loss of human life, or lawsuits resulting from negligence.
The Need for Trusted AI Solutions
Spending on Artificial Intelligence (AI) is expected to more than double from $35 billion in 2019 to $79 billion in 2022, according to IDC forecasts, reflecting the enormous potential societal benefits of AI. Yet broad adoption of AI systems will not come from the benefits alone but from the ability to trust these dynamically evolving digital systems. Trust is the foundation of all digital systems. Without trust, artificial intelligence and machine learning systems cannot deliver on their potential value.
Ultimately, Healthcare organizations and end users need to be able to answer key questions about these black box AI solutions like:
- How did the system predict what it predicted? (Explanation)
- Has the AI system or model been unfair to a particular group? (Fairness/Bias)
- How easily can the model be fooled? (Robustness)
- If a person got an unfavorable outcome from the model(s), what can they do to change that? (Counterfactuals)
- Can the solution provides relevant results for different business and IT stakeholders?
(e.g. Clinician End Users, Risk Executives, Line of Business Owners, Data Scientists/IT)
The Need for Trusted AI Solutions
Spending on Artificial Intelligence (AI) is expected to more than double from $35 billion in 2019 to $79 billion in 2022, according to IDC forecasts, reflecting the enormous potential societal benefits of AI. Yet broad adoption of AI systems will not come from the benefits alone but from the ability to trust these dynamically evolving digital systems. Trust is the foundation of all digital systems. Without trust, artificial intelligence and machine learning systems cannot deliver on their potential value.
