Ray Quinney & Nebeker’s Post

🚀 𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗠𝗲𝗱𝗶𝗰𝗼-𝗟𝗲𝗴𝗮𝗹 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁𝘀 𝗶𝘀 𝗛𝗲𝗿𝗲! 🤖⚖️ Are you ready for the AI revolution in healthcare? It's not just changing how we treat patients - it's transforming the entire medico-legal landscape! 🌟 Here's what you need to know: 📊 𝗔𝗜 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗶𝗻 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗶𝘀 𝗦𝗸𝘆𝗿𝗼𝗰𝗸𝗲𝘁𝗶𝗻𝗴: • 10% of healthcare organizations are at mid-stage AI adoption • 14% are at early-stage adoption • The AI medical device market is projected to grow rapidly through 2032[4] 🧠 𝗔𝗜 𝗶𝘀 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗠𝗲𝗱𝗶𝗰𝗼-𝗟𝗲𝗴𝗮𝗹 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁𝘀: 1. Improved accuracy in Independent Medical Examiner (IME) reports 2. Faster processing of large volumes of medical data 3. Reduced bias in analysis, ensuring fairer outcomes[1] 💡 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗜𝗺𝗽𝗮𝗰𝘁: • AI can identify when a patient has reached maximum medical improvement • It can trigger permanent impairment assessments automatically • AI is streamlining the process of calculating impairment ratings[2] ⚖️ 𝗟𝗲𝗴𝗮𝗹 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: • New types of expert witnesses may be needed for AI-related cases • Judges and lawyers will require new skills to navigate AI in healthcare • Legislation may need to evolve to address AI-specific scenarios[3] 🔍 𝗠𝘆 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲: As a medicolegal professional, I've witnessed firsthand how AI is revolutionizing our field. In one recent case, AI analysis helped uncover crucial patterns in medical records that led to a more accurate assessment of a complex injury claim. 👥 Let's Discuss: How do you see AI impacting medico-legal assessments in your practice? What challenges and opportunities do you foresee? #AIinHealthcare #MedicoLegal #HealthTech #LegalInnovation #FutureOfLaw

'How is ʻUnexplainable’ and Non-transparent AI Affecting Healthcare Delivery?' by Vera Lucia RAPOSO in 'Oslo Law Review': 'The key issues that require attention encompass accountability, biases, erroneous results, lack of justification of medical decisions, and the erosion of trust.' Refers to European Parliamentary Research Service publication on 'Artificial intelligence in healthcare', available here: https://lnkd.in/eJ-89Kjm #EPRS #aihealthcare #ai

The introduction of AI note-taking in healthcare, as exemplified by the case of the Ontario family doctor, sparks multifaceted discussions. One pivotal point centers on the balance between efficiency and quality in medical documentation. While AI systems promise to streamline note-taking processes, concerns loom regarding the potential compromise in the thoroughness and accuracy of medical records. This juxtaposition underscores the need for careful consideration and oversight to ensure that the integration of AI enhances, rather than detracts from, the standard of care provided to patients. Another critical aspect revolves around legal compliance and billing accuracy. Accurate documentation is not only essential for maintaining legal compliance, particularly under regulations like HIPAA, but also for proper billing and reimbursement. AI note-taking systems can play a crucial role in ensuring that all necessary information is captured and appropriately coded, mitigating risks of compliance breaches and billing errors. Moreover, the impact of AI note-taking on patient care is subject to debate. While proponents argue that time saved through AI transcription can be redirected towards enhancing patient care, skeptics raise concerns about potential trade-offs between efficiency gains and the quality of patient interactions. Striking a delicate balance between administrative tasks and direct patient care remains paramount in leveraging AI technologies effectively within healthcare settings. Additionally, discussions delve into broader societal implications, such as patient data privacy and trust in healthcare institutions. Patients' apprehensions about data sharing with third parties, coupled with the legal ramifications of HIPAA violations, underscore the importance of robust privacy policies and compliance measures. Fostering transparent communication and actively addressing patient concerns are imperative steps towards cultivating trust and ensuring the ethical use of AI in healthcare. Ultimately, the integration of AI note-taking in healthcare heralds both opportunities and challenges. By navigating these complexities with diligence and foresight, healthcare stakeholders can harness the transformative potential of AI while upholding the highest standards of patient care and data privacy. #AINoteTaking #HealthcareAI #MedicalDocumentation #HIPAACompliance #PatientCare #DataPrivacy #EthicalAI

🤖 CHAPTER 9: "Liability for the Use of Artificial Intelligence in Medicine" by Nicholson Price, Sara Gerke, and I. Glenn Cohen (#OpenAccess HERE ▶ https://lnkd.in/d3eVZPHS) dives into the evolving challenges of #liability in healthcare as #AI becomes an integral part of medical practice. The chapter explores legal principles, systemic complexities, and the potential for future shifts in #liabilitynorms. 🚨 Key challenges in AI liability: ⚖️ Physician liability: Physicians face dilemmas when AI recommendations deviate from the #standardofcare. For example, rejecting correct AI advice may result in injury but often shields physicians from liability under existing laws. 🏥 Institutional liability: Hospitals must navigate derivative #liability for employees' use of AI and direct liability for decisions involving the selection, retention, and supervision of #AIsystems. 🖥️ Developer liability: AI developers face questions about #negligence and #productliability, especially in cases involving biased datasets or "black-box" #algorithms that lack transparency. 🔒 Regulatory considerations: 📜 Evolving standard of care: As AI adoption grows, following AI recommendations could increasingly be considered part of the #standardofcare, reshaping #liabilitydynamics. 📄 FDA and EU frameworks: Regulatory approaches in the #US and #Europe, including the #FDA's oversight and the EU's AI Liability Directive, highlight differing strategies to address AI-related harm. 💡 Key insights: 🔄 AI adoption incentives: Liability concerns may discourage #innovation, especially when developers and providers lack clarity on potential risks. 🌍 Global perspective: The chapter draws lessons from emerging #international frameworks, emphasizing the need for harmonized approaches to address cross-border AI applications in healthcare. 📖 Chapter 9 is part of the Research Handbook on Health, AI, and the Law (with I. Glenn Cohen). It provides essential insights into navigating the legal landscape of #medicalAI, offering valuable guidance for developers, institutions, and policymakers alike. Edward Elgar Publishing Hamad Bin Khalifa University College of Law, Hamad Bin Khalifa University (HBKU) The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School Ben Booth #AI #HealthLaw #MedicalAI #Liability #AIRegulation #HealthcareInnovation #DataPrivacy #Ethics #FDA #EUDirectives

AI is good and growing fast but we are still in the developmental stages. Companies need to be careful with the claims they make. Texas Strikes Settlement with Dallas AI Firm Over Misleading Healthcare Tech Claims 📢 Issue: The State of Texas, through its Attorney General, filed a petition for the approval and entry of an Assurance of Voluntary Compliance (AVC) with Pieces Technologies, Inc. The key issue involves whether Pieces Technologies, Inc. engaged in deceptive practices under the Texas Deceptive Trade Practices - Consumer Protection Act (DTPA) by making false or misleading representations about the accuracy of its artificial intelligence (AI) products used in healthcare. 📢 Rule: The Texas Deceptive Trade Practices Act (DTPA) prohibits false, misleading, or deceptive practices in the advertising and sale of goods or services (Tex. Bus. & Com. Code §§ 17.41-.63). Specifically, Section 17.47 allows the Consumer Protection Division of the Attorney General’s office to investigate and take action on violations. 📢 Application: Allegations by the State of Texas: Pieces Technologies developed and marketed AI products intended to assist healthcare providers in treating patients. It claimed its products had very low error rates (referred to as "hallucination rates") in creating outputs like clinical notes. The state alleged these claims were false or misleading, violating the DTPA, since the AI could produce incorrect or misleading outputs. Response by Pieces Technologies: The company denied any wrongdoing or liability, stating that its claims about the AI’s accuracy were accurate and did not violate any laws. Assurance of Voluntary Compliance: To settle the matter without prolonged litigation, Pieces Technologies agreed to a series of actions, including: Providing clear and conspicuous disclosures in all marketing materials regarding the accuracy and metrics of its AI products. Prohibiting misleading or unsubstantiated representations about the accuracy or testing of its AI products. Ensuring transparency with customers about the potential risks and limitations of its products. Complying with the AVC terms for five years and implementing internal processes to monitor compliance. 📢 Conclusion: Pieces Technologies agreed to settle the matter by entering into the AVC, which does not constitute an admission of liability. The company agreed to specific practices and disclosures to comply with Texas law, as outlined in the AVC, and the court’s approval was sought for the AVC. --------------- Sanjay Juneja, M.D. Douglas Flora, MD, LSSBB, FACCC Nixon Gwilt Law Sean Weiss Ronald Chapman II, Esq. LLM Michael Crocker Rebecca E. Gwilt David Penberthy, MD MBA FACCC VMG Health ECG Management Consultants Etyon HLTH Inc. Aashish Shah ------ https://lnkd.in/enduqyDe.

🌐 𝐀𝐈 𝐢𝐧 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞: 𝐖𝐡𝐨'𝐬 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐥𝐞 𝐖𝐡𝐞𝐧 𝐀𝐈 𝐅𝐚𝐢𝐥𝐬? 🤔 AI is transforming #Healthcare, but accountability remains unclear. With regulations like the AI Act by the #EuropeanCommission and frameworks like DISHA in India, the liability for adverse outcomes is still a grey area. 🤖 Should clinicians or AI developers be held responsible for errors? 📊 Read more on the regulatory & legal hurdles in adopting #AIinHealthcare and how they might impact innovation. 🚀 🔗Read More - https://lnkd.in/dwiY4id3 🖊️Written By - GANAPATHY Krishnan   🌐Stay Informed with our Latest Stories – https://lnkd.in/gMUPB2Zp #AI #HealthTech #DigitalHealth #Innovation #AIRegulations #ArtificialIntelligence

Last night I attended (virtually) the Medical Protection Society - Spotlight on Risk & Medicolegal & Ethical Issue Conference. One of the most interesting and thought-provoking presentations was by:  Dr Gilberto Leung - President, HK Academy of Medicine. The future of clinical practice in the AI-driven era. From my perspective, numerous issues regarding AI remain open, it's an entity that deserves deep respect and caution. AI has the potential to evolve healthcare and improve patient outcomes, yet it poses several challenging questions for healthcare professionals. Exploring the moral, legal, and ethical responsibilities of healthcare practitioners utilising AI in patient care includes determining appropriate levels of patient disclosure, managing consent, and safeguarding patient privacy. It is also crucial to address liability for errors—whether it falls on developers, manufacturers, or users—as well as to keep legislation abreast of rapid AI advancements. Moreover, ensuring unbiased data input into AI applications is vital to prevent harm from biased outcomes. How can we ensure that AI is used in a safe, ethical, and transparent manner? How can we balance the benefits and risks of AI, and who should be accountable for its outcomes? How can we foster trust and collaboration between human and artificial intelligence? These are some of the challenges that we need to address as we enter the AI-driven era of clinical practice. Some of the discussion and questions that Dr Leung raised are not only relevant for healthcare practitioners, but also for patients, policymakers, and society as a whole. It was a fascinating talk last night and I have much to learn. Thank you to MPS for arranging the conference and for the invite and thank you to the speakers. Also thank you to David OWENS for the recent articles you have been writing about AI and digital health. #AI #digitalhealth #advancesinhealthcare

Bellwether for upcoming AI in Healthcare regulations? California passed 18 new AI bills, with key ones impacting healthcare: AB-3030: Requires health care providers to disclose when they use GenAI to communicate with patients, particularly when the messages contain clinical information. SB-1120: Limits healthcare AI use, ensuring physician review of decisions made or assisted by a health plan’s AI decision making tools or algorithms. SB-1223: Expands "sensitive data" to include neural data, preventing AI companies from using brain data for hyper-personalized models. Smart first steps towards responsible AI regulation-expect more to follow. #AIinHealthcare #DataPrivacy #CaliforniaLaws #NeuralData #healthcare #pharmaceutical #lifesciences #ai #genai

1. California Assembly Bill 3030, signed on September 28, 2024, regulates the use of generative AI in healthcare. 2. The law takes effect on January 1, 2025, introducing requirements for healthcare providers utilizing AI for patient communications. 3. AI-generated communications must include a disclaimer identifying them as AI-generated and provide contact instructions for reaching a human provider. 4. The law does not apply to AI communications approved by licensed healthcare providers or to administrative matters like scheduling and billing. 5. GenAI is defined as AI that generates synthetic content, distinct from predictive models. 6. Violations of AB 3030 could lead to disciplinary actions from medical boards or enforcement under health safety codes. 7. While the law aims to reduce administrative burdens and enhance transparency, it does not regulate the specifics of clinical content. 8. Concerns remain regarding biases in AI-generated content, the phenomenon of "hallucination," and privacy issues related to patient data.

𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲: 𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗶𝗻𝗴 𝗟𝗲𝗴𝗮𝗹 𝗪𝗮𝘁𝗲𝗿𝘀 🛞 How do we ensure our legal frameworks keep pace with healthcare AI, instead of acting like they're stuck in the floppy disk era while AI is streaming 4K video? Navigating the potential of AI to transform healthcare against outdated laws feels a bit like trying to stream a blockbuster on dial-up. According to a recent McKinsey Health Institute report, "Current legal structures adapt at a pace that lags significantly behind technological advancements, increasing the risk of stifling innovation." ⚙️ Without updates to these frameworks, we risk entangling our healthcare innovations in bureaucratic red tape, potentially stalling advancements and degrading patient care quality. Here's how we can smooth out the legal landscape for AI in healthcare: 📝 Form Multi-disciplinary Teams: Create a coalition of legal experts, tech wizards, and healthcare professionals to navigate AI's complexities. 📝 Pilot Regulatory Sandboxes: Trial AI technologies in controlled environments to ensure compliance and effectiveness before full deployment. 📝 Educate Decision-makers: Boost training for lawyers, judges, and policymakers on AI in healthcare, sharpening their ability to craft informed policies. By taking these steps, we can ensure that our legal frameworks facilitate rather than hinder healthcare innovation. What strategies do you think could help us better integrate diverse expertise to refine AI’s legal framework in healthcare? 🔽🔽 #healthcare #innovation #ai #lawandlegislation #publicspeaking #wellness