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Artificial Intelligence & Health Care: State Outlook and Legal Update for 2024
Table of Contents
as Congress holds hearings on the growing use of artificial intelligence (AI) in the health care sector, states are considering and enacting legislation to restrict the use or implementation of such technology. While states’ actions are aimed at protecting patient health and privacy, they risk creating a patchwork of regulations that are difficult to navigate.
Current Regulatory Landscape of AI in Public Health & Health Care
This document details the statutes, regulations, and other exploratory actions taken by Federal and state governments regarding the use of artificial intelligence in health care and public health. It includes data on the National Artificial Intelligence Initiative Act of 2020 and the first report on “Strengthening the Regulatory Framework for Artificial Intelligence in Health Care.”
Healthy Technology Act of 2025: AI Prescribing Bill Introduced in Congress
This bill, introduced by Representative Schweikert, aims to leverage AI in healthcare to streamline the drug approval process. The legislation seeks to use AI to determine optimal dosages and reduce the number of clinical trials required, possibly shortening the time and cost of bringing new drugs to market.
These sources provide a comprehensive overview of the current state and regulatory landscape of AI in healthcare.Based on the provided text, here are some key points and suggestions for AI policy and legislation in healthcare:
- Bias and Fairness: Both speakers emphasize the importance of addressing biases in AI to prevent inappropriate care and worse outcomes for patients.Legislators should consider including provisions that mandate regular audits and assessments of AI systems for biases and fairness.
- Innovation and Competition: Legislators should aim to strike a balance between regulation and fostering innovation. They should ensure that regulations do not stifle competition or hinder the development of new AI products and services.
- Liability: The text mentions the need to consider liability when doctors use AI tools. Legislators could explore creating a clear legal framework that outlines when doctors can be held accountable for following or not following AI recommendations.
- Private Sector Models: To inform federal government delivery of AI in healthcare, legislators could look at prosperous private sector models. Some examples include:
– Interoperability Standards: Adopting standards that allow AI tools from different vendors to integrate seamlessly with electronic health records (EHRs), as promoted by organizations like HL7 and the ONC.
– AI in Drug Discovery and Development: Learning from companies like BenevolentAI and Insilico Medicine, which use AI to accelerate drug discovery and development.
– AI in Clinical Decision Support: Studying AI-driven clinical decision support systems,like those developed by companies like IDx and Viz.ai, which have received FDA clearance.
- Flexible Legislation: Given the fast-paced nature of AI development, legislators should consider creating flexible legislation that can adapt to new technologies and use cases. This could involve regular reviews and updates of the legislation or incorporating mechanisms for stakeholder input.
- Collaboration: Legislators should collaborate with experts in AI,healthcare,and ethics to ensure that any legislation is well-informed,practical,and effective.
legislators should focus on addressing biases, fostering innovation, clarifying liability, learning from successful private sector models, creating flexible legislation, and collaborating with experts to develop effective AI policies in healthcare.Based on the provided text, here are some key points about AI in the private sector and the Veterans Affairs (VA) department:
- Private Sector:
– Companies like Oracle, OpenAI, and SoftBank are collaborating to advance AI technology.
– There’s a competitive global market for AI, with private companies still exploring better ways to utilize it.
– Liability is a significant concern in the private sector, especially in healthcare.It’s unclear who would be responsible if an AI system causes harm – the AI developer, the healthcare provider, or the patient.
- Veterans Affairs (VA):
– the VA is working on integrating AI into their electronic health systems to improve data accuracy and sharing.
– The focus is on ensuring that health records (HRs) are up-to-date and can be easily transferred between veterans.
– This effort is part of a broader trend in hospitals to make patient data more accessible and accurate.while AI is being actively pursued by both the private sector and the VA,there are still significant challenges to overcome,particularly around liability and data integration.
AI in Healthcare: Challenges and Innovations
The integration of artificial intelligence (AI) in healthcare is a hotly debated topic, especially as it pertains to legislation and ethics. Policymakers must ensure that any legislation surrounding AI in healthcare is well-informed, practical, and effective.
- Private Sector:
- Companies like [Oracle](https://www.oracle.com/), [OpenAI](https://openai.com/), and [SoftBank](https://group.softbank/en/corp/) are collaborating to advance AI technology.
– There’s a competitive global market for AI, with private companies still exploring better ways to utilize it.
– Liability is a important concern in the private sector, especially in healthcare.It’s unclear who would be responsible if an AI system causes harm – the AI developer, the healthcare provider, or the patient.
- Veterans Affairs (VA):
– The VA is working on integrating AI into their electronic health systems to improve data accuracy and sharing.
– The focus is on ensuring that health records (HRs) are up-to-date and can be easily transferred between veterans.
– This effort is part of a broader trend in hospitals to make patient data more accessible and accurate.
While AI is being actively pursued by both the private sector and the VA, there are still significant challenges to overcome, especially around liability and data integration.
Interview with a Healthcare and AI Expert
Editor: In your opinion, what are the key challenges that legislators should address when drafting AI legislation in healthcare?
Guest: Legislators should focus on addressing biases, fostering innovation, clarifying liability, learning from successful private sector models, creating flexible legislation, and collaborating with experts to develop effective AI policies in healthcare.
Editor: Can you elaborate on the challenges of liability in the private sector? How can this be addressed in legislation?
Guest: Liability is a complex issue. Laws need to clearly define roles and responsibilities. As a notable example, if an AI system causes harm, it should be outlined whether the liability falls to the AI developer, the healthcare provider, or the patient. This clarity will help in better risk management and will encourage innovation without compromising safety.
Editor: What is the importance of integrating AI in the VA’s electronic health system?
Guest: Integrating AI in the VA’s electronic health system is crucial for improving data accuracy and sharing. Ensuring that health records are up-to-date and easily transferable is essential for providing better and more collaborative care for veterans.
Editor: what are the most crucial considerations for hospitals adopting AI in their data systems?
Guest: Hospitals should prioritize data security,patient consent,and compliance with existing healthcare regulations. Thay should also focus on training healthcare professionals on using AI tools effectively and engaging with experts to navigate ongoing challenges.
