Attention all professionals involved in the development, marketing, purchasing, implementation or monitoring of medical technologies equipped with AI and machine learning: you owe it to healthcare consumers to keep certain principles in mind in everything you do with these technologies.
The principles, of which there are eight, are set out in a white paper published by the world’s largest medical device association.
“The entire healthcare ecosystem should be aware of the opportunity to better serve patients while sharing the commitment to protecting safety, security and privacy through the responsible development of AI,” explains the AdvaMed association, based in Washington. “These stakeholders include, but are not limited to, patients, healthcare professionals, healthcare providers, IT system integrators, healthcare IT developers, IT vendors, medical device manufacturers and the regulators. »
Here are excerpts from five of the eight must-haves on AdvaMed’s mind.
1. Leverage existing regulatory frameworks and promote international alignment to ensure rapid patient access to innovative AI/ML-based medical technologies.
Current pre- and post-market regulatory frameworks are “fully capable of ensuring the safety and effectiveness of AI/ML-enabled devices, whether they use locked or adaptive AI algorithms,” the authors write. More:
“FDA oversight is guided by a risk-based framework that includes a rigorous premarket review process that evaluates the performance, reliability, and safety of medical devices, as well as extensive monitoring and monitoring requirements. post-market surveillance of devices after authorization for sale. »
2. Protect patient data privacy with transparency and consent.
The data required to build AI models and deliver AI-based solutions “should be collected transparently with appropriate informed notice and authorization,” AdvaMed says.
“Technology innovators must protect patient privacy in accordance with all applicable data privacy laws and regulations and implement industry best practices, international consensus standards and organizational measures to ensure security, data integrity and confidentiality.”
3. Allow access to data and their use for the benefit of patients.
“There should be a clear definition of which stakeholders can access patient data and for what purposes,” the authors say. “Ensuring the highest ethical and trustworthy standards in data management must be a priority. »
“Healthcare stakeholders, innovative staff and external vendors should collaborate to achieve a clear understanding of what data is collected, how it is used and how it is protected. »
4. Develop and deploy AI/ML-based solutions responsibly and mitigate unwanted bias in AI/ML-based medical technologies.
To identify and address potential unwanted biases in AI-enabled devices, “high-quality, representative datasets of target patient groups are essential,” the authors argue. “Manufacturers can mitigate unwanted bias before product release through careful and thorough data collection, analysis and curation. »
“Once on the market, ongoing monitoring, evaluation and/or validation may be necessary. Manufacturers are responsible for post-marketing requirements after deployment and reporting issues to the FDA to ensure continued safety and effectiveness.
5. Promote access and adoption of AI technologies to benefit patients.
“The current CMS reimbursement framework is based on a statutory foundation that does not contemplate the need to consider coverage, coding, or payment for these types of new diagnostics and therapeutic technologies, including health services based on algorithms.”
“Reimbursement frameworks should be established to capture the full value of new AI-based technologies, including long-term financial savings associated with better health outcomes, earlier disease detection and “effectiveness gained by health care providers.”
Also on the AdvaMed results list:
- Leveraging digital and AI-based health solutions to facilitate and promote access to healthcare in rural and underserved communities to improve health equity.
- Educate the public, patients, and clinicians on the roles and value of AI/ML-based health technologies while prioritizing clinician and user education on AI/ML-based technologies.
- Provide transparency, essential to patient-centered care, that contains the appropriate level of information needed to ensure safe and effective use of the device.