Enablon’s Post

Regulatory Considerations on Artificial Intelligence for Health! Recommendations: I. Documentation and transparency 1. Consider pre-specifying and documenting the intended medical purpose and development process 2. Consider a risk-based approach also for the level of documentation and record-keeping utilized for the development and validation of AI systems II. Risk management and AI systems development lifecycle approach recommendations 1. Consider a total product lifecycle approach throughout all phases in the life of a medical device 2. Consider a risk management approach that addresses risks associated with AI systems III. Intended use and analytical and clinical validation recommendations 1. Consider providing transparent documentation of the intended use of the AI system 2. Consider demonstrating performance beyond the training dataset through external, analytical validation in an independent dataset 3. Consider a graded set of requirements for clinical validation based on risk 4. Consider a period of more intense post-deployment monitoring through post-market management and market surveillance for high-risk AI systems IV. Data quality recommendations 1. Consider whether available data are of sufficient quality to support the development of the AI system that can achieve the intended purpose 2. Consider deploying rigorous pre-release evaluations for AI systems to ensure that they will not amplify any of relevant issues, such as biases and errors 3. Consider careful design or prompt troubleshooting to help early identification of data quality issues 4. Consider mitigating data quality issues that arise in health-care data and the associated risks 5. Consider working with other stakeholders to create data ecosystems that can facilitate the sharing of good-quality data sources V. Privacy and data protection recommendations 1. Consider privacy and data protection during the design and deployment of AI systems 2. Consider gaining a good understanding of applicable data protection regulations and privacy laws early in the development process and ensure that the development process meets or exceeds such legal requirements 3. Consider implementing a compliance programme that addresses risks and develop privacy and cybersecurity practices and priorities that take into account potential harm and the enforcement environment Vl. Engagement and collaboration recommendations 1. Consider the development of accessible and informative platforms that facilitate engagement and collaboration, where applicable and appropriate, among key stakeholders of the AI innovation and deployment roadmap 2. Consider streamlining the oversight process for AI regulation through engagement and collaboration in order potentially to accelerate practice-changing advances in AI Check out the World Health Organization report her (Thanks Eva von Mühlenen, LL.M. for the share): https://lnkd.in/dQ-MKc-8 #healthcare #innovation #healthtech #digitalhealth #ai #ml #genai

Regulatory Considerations on Artificial Intelligence for Health! Recommendations: I. Documentation and transparency 1. Consider pre-specifying and documenting the intended medical purpose and development process 2. Consider a risk-based approach also for the level of documentation and record-keeping utilized for the development and validation of AI systems II. Risk management and AI systems development lifecycle approach recommendations 1. Consider a total product lifecycle approach throughout all phases in the life of a medical device 2. Consider a risk management approach that addresses risks associated with AI systems III. Intended use and analytical and clinical validation recommendations 1. Consider providing transparent documentation of the intended use of the AI system 2. Consider demonstrating performance beyond the training dataset through external, analytical validation in an independent dataset 3. Consider a graded set of requirements for clinical validation based on risk 4. Consider a period of more intense post-deployment monitoring through post-market management and market surveillance for high-risk AI systems IV. Data quality recommendations 1. Consider whether available data are of sufficient quality to support the development of the AI system that can achieve the intended purpose 2. Consider deploying rigorous pre-release evaluations for AI systems to ensure that they will not amplify any of relevant issues, such as biases and errors 3. Consider careful design or prompt troubleshooting to help early identification of data quality issues 4. Consider mitigating data quality issues that arise in health-care data and the associated risks 5. Consider working with other stakeholders to create data ecosystems that can facilitate the sharing of good-quality data sources V. Privacy and data protection recommendations 1. Consider privacy and data protection during the design and deployment of AI systems 2. Consider gaining a good understanding of applicable data protection regulations and privacy laws early in the development process and ensure that the development process meets or exceeds such legal requirements 3. Consider implementing a compliance programme that addresses risks and develop privacy and cybersecurity practices and priorities that take into account potential harm and the enforcement environment Vl. Engagement and collaboration recommendations 1. Consider the development of accessible and informative platforms that facilitate engagement and collaboration, where applicable and appropriate, among key stakeholders of the AI innovation and deployment roadmap 2. Consider streamlining the oversight process for AI regulation through engagement and collaboration in order potentially to accelerate practice-changing advances in AI Check out the World Health Organization report her (Thanks Eva von Mühlenen, LL.M. for the share): https://lnkd.in/dQ-MKc-8 #healthcare #innovation #healthtech #digitalhealth #ai #ml #genai

Regulatory Considerations on Artificial Intelligence for Health! Recommendations: I. Documentation and transparency 1. Consider pre-specifying and documenting the intended medical purpose and development process 2. Consider a risk-based approach also for the level of documentation and record-keeping utilized for the development and validation of AI systems II. Risk management and AI systems development lifecycle approach recommendations 1. Consider a total product lifecycle approach throughout all phases in the life of a medical device 2. Consider a risk management approach that addresses risks associated with AI systems III. Intended use and analytical and clinical validation recommendations 1. Consider providing transparent documentation of the intended use of the AI system 2. Consider demonstrating performance beyond the training dataset through external, analytical validation in an independent dataset 3. Consider a graded set of requirements for clinical validation based on risk 4. Consider a period of more intense post-deployment monitoring through post-market management and market surveillance for high-risk AI systems IV. Data quality recommendations 1. Consider whether available data are of sufficient quality to support the development of the AI system that can achieve the intended purpose 2. Consider deploying rigorous pre-release evaluations for AI systems to ensure that they will not amplify any of relevant issues, such as biases and errors 3. Consider careful design or prompt troubleshooting to help early identification of data quality issues 4. Consider mitigating data quality issues that arise in health-care data and the associated risks 5. Consider working with other stakeholders to create data ecosystems that can facilitate the sharing of good-quality data sources V. Privacy and data protection recommendations 1. Consider privacy and data protection during the design and deployment of AI systems 2. Consider gaining a good understanding of applicable data protection regulations and privacy laws early in the development process and ensure that the development process meets or exceeds such legal requirements 3. Consider implementing a compliance programme that addresses risks and develop privacy and cybersecurity practices and priorities that take into account potential harm and the enforcement environment Vl. Engagement and collaboration recommendations 1. Consider the development of accessible and informative platforms that facilitate engagement and collaboration, where applicable and appropriate, among key stakeholders of the AI innovation and deployment roadmap 2. Consider streamlining the oversight process for AI regulation through engagement and collaboration in order potentially to accelerate practice-changing advances in AI Check out the World Health Organization report her (Thanks Eva von Mühlenen, LL.M. for the share): https://lnkd.in/dQ-MKc-8 #healthcare #innovation #healthtech #digitalhealth #ai #ml #genai