WHAT ARE THE BENEFITS OF AI?
The advancement of AI in healthcare can complement physician decision-making. Data in healthcare is often unstructured and there is a large amount to source and scale for individual assessment. The use of AI can allow for improved efficiencies, streamline processes and information sharing, and enhance decisions – all in support of patient care.
WHAT ARE THE MISCONCEPTIONS OR LIMITATIONS OF AI?
AI in healthcare is very exciting because of the benefits it can offer to enhance the physician’s ability to care for the patient. However, it is important that enthusiasm doesn’t turn into misguided use of information. AI is not ready to work on its own. AI and physicians must work together to gain the greatest benefits for improving patient care. There are unrealistic predictions and a false sense of what AI can do as a complement in clinical practice. There are precise methods in testing and areas to evaluate the suitability for AI in clinical application. Here are some of the limitations of AI:
- Explainability and Transparency – Machine Learning, and in particular, deep learning, can act as a black box which can make it difficult to understand how the AI system arrived at the decision. Explainability is a process that allows end-users of the application to describe what the AI model is doing to get to a decision. This step is helpful to better comprehend its expected impact. There is ongoing effort to improve transparency of AI algorithms in clinical practice.
- Bias – The purpose of the AI solution might warrant evaluation of the data sources to eliminate bias that could interfere with the results. There is still a lot of research necessary to determine the implications of potential bias of the training data compared to real-life.
- Cost – Typically training deep learning models either requires purchasing expensive computational resources with expensive and powerful computers or renting them from cloud providers.
- Regulatory – The Regulatory landscape for AI-based products and services is still evolving. For example, FDA is looking to develop a regulatory framework to support the iterative nature of AI-based software, while still ensuring the continued safety and effectiveness of AI-solutions.
- Privacy – It is important to use only anonymized or deidentified data with appropriate patient consent and meet other compliance with applicable laws and regulations. This patient level information is not necessary for AI development and therefore there is no issue to remove it from visibility. Privacy and security are important principles in AI-enabled therapies to respect and protect the personal and sensitive information of users, patients, clinicians, and partners throughout the total product lifecycle.
There are many areas to evaluate when considering the application of AI in healthcare. The above are some considerations on the appropriate solutions that can benefit the physician, healthcare system, and patient outcomes.
HOW DO WE ENSURE THE SUITABILITY OF AI IN HEALTHCARE?
It is important to use high quality and variability of data within AI algorithms to help ensure it aligns to the real-life use cases. The data should be tested properly to validate the accuracy of outcomes.
Questions that should be considered include:
- Will the product/customer/patient benefit from the application of AI for your use case?
- Can you collect enough data to support the performance that you need?
- How has the data been tested to prove a desired outcome?
- Is your data deidentified/anonymized?
- Do you have patient consent for R&D development?
- What are the benefits of your AI application?
- What risks does your AI application introduce?
- How does your AI interact with the physician?
- How does the physician interact with the AI application?
Disclaimer: All content from healthcare professionals is their individual conclusions, unless otherwise cited. All speaker or author engagement for content is noted to acknowledge funding from Covidien LP, a Medtronic company, for any consulting engagement.