# AI Adoption Barriers * **Definition:** AI Adoption Barriers refer to the challenges and obstacles that healthcare organizations face when integrating artificial intelligence technologies into their operations. These barriers can include issues related to data privacy and security, lack of interoperability, insufficient infrastructure, resistance to change among staff, high costs of implementation, and regulatory compliance concerns. * **Taxonomy:** Healthcare Topics / AI Adoption Barriers ## News * Selected news on the topic of **AI Adoption Barriers**, for healthcare technology leaders * 2K news items are in the system for this topic * Posts have been filtered for tech and healthcare-related keywords | Date | Title | Source | | --- | --- | --- | | 5/27/2025 | [**VC Survey: The biggest impacts of AI on health care? - STAT News**](https://www.statnews.com/2025/05/27/health-tech-news-novo-nordisk-venrock-ro-hinge-livestrong-otsuka-anthropic/) | [[STAT]] | | 5/27/2025 | [**VC Survey: The biggest impacts of AI on health care?**](https://www.statnews.com/2025/05/27/health-tech-news-novo-nordisk-venrock-ro-hinge-livestrong-otsuka-anthropic/?utm_campaign=rss) | [[STAT]] | | 5/19/2025 | [**Medical Telemetry System Market Expansion 2025: Top Emerging Regions to Watch**](https://www.linkedin.com/pulse/medical-telemetry-system-market-expansion-2025-top-emerging-0nmse/) | [[Linkedin]] | | 5/8/2025 | [**Hospital Asset Management Market is expected to generate a revenue of USD 414.18 ...**](https://www.prnewswire.com/news-releases/hospital-asset-management-market-is-expected-to-generate-a-revenue-of-usd-414-18-billion-by-2032--globally-at-30-60-cagr-verified-market-research-302450001.html) | [[PR Newswire]] | | 5/8/2025 | [**Hospital Asset Management Market is expected to generate a revenue of USD 414.18 ...**](https://finance.yahoo.com/news/hospital-asset-management-market-expected-141500478.html) | [[Yahoo Finance]] | | 4/22/2025 | [**How AI in Cardiology Is Quietly Rewriting the Rules of Heart Care - by Apoorv Gehlot**](https://medium.com/@apoorv-gehlot/how-ai-in-cardiology-is-quietly-rewriting-the-rules-of-heart-care-0f2244af7b2f) | [[Medium]] | | 4/7/2025 | [**AI being used more often at point of care, KLAS research shows - Healthcare IT News**](https://www.healthcareitnews.com/news/ai-being-used-more-often-point-care-klas-research-shows) | [[Healthcare IT News]] | | 3/27/2025 | [**HiPaaS Hosted its 2nd Hospital AI Conference on March 19th, 2025 at VC Nest in Palo Alto**](https://www.prweb.com/releases/hipaas-hosted-its-2nd-hospital-ai-conference-on-march-19th-2025-at-vc-nest-in-palo-alto-302412799.html) | [[PRWeb]] | | 2/27/2025 | [**Philips Champions Digital Transformation at BioAsia 2025 - ICT&health International**](https://ictandhealth.com/ai-health-news/philips-champions-digital-transformation-at-bioasia-2025) | [[ICT and Health]] | | 2/12/2025 | [**Comprehensive 25-year history of electronic health records and their role in medical research assembled**](https://medicalxpress.com/news/2025-02-comprehensive-year-history-electronic-health.html) | [[MedicalXpress]] | | 1/30/2025 | [**AI's Expanding Role in Healthcare for 2025**](https://thehealthcaretechnologyreport.com/ais-expanding-role-in-healthcare-for-2025/) | [[Healthcare Technology Report]] | | 1/2/2025 | [**AI in healthcare: What to expect in 2025**](https://www.chiefhealthcareexecutive.com/view/ai-in-healthcare-what-to-expect-in-2025) | [[Chief Healthcare Executive]] | | 12/27/2024 | [**AI Tech: How AI Will Affect The Economy In 2025 - by john tsantalis - Dec, 2024 - Medium**](https://medium.com/@johntsantalis/ai-tech-how-ai-will-affect-the-economy-in-2025-b04f100a74ff) | [[Medium]] | | 12/18/2024 | [**Healthcare AI News 12/18/24**](https://histalk2.com/2024/12/18/healthcare-ai-news-12-18-24/) | [[HISTalk]] | | 12/10/2024 | [**Ataccama: Businesses will fail without AI adoption, 72f data experts say - PR Newswire**](https://www.prnewswire.com/news-releases/ataccama-businesses-will-fail-without-ai-adoption-72-of-data-experts-say-302327738.html) | [[PR Newswire]] | | 12/7/2024 | [**Weekly Roundup - December 7, 2024 - Healthcare IT Today**](https://www.healthcareittoday.com/2024/12/07/weekly-roundup-december-7-2024/) | [[Healthcare IT Today]] | | 12/6/2024 | [**Medscape and HIMSS Release 2024 Report on AI Adoption in Healthcare - PR Newswire**](https://www.prnewswire.com/news-releases/medscape-and-himss-release-2024-report-on-ai-adoption-in-healthcare-302324936.html) | [[PR Newswire]] | | 12/5/2024 | [**Navigating AI Adoption in Healthcare: Overcoming Key Challenges for Sustainable Growth**](https://www.healthcareittoday.com/2024/12/05/navigating-ai-adoption-in-healthcare-overcoming-key-challenges-for-sustainable-growth/) | [[Healthcare IT Today]] | | 11/19/2024 | [**Health system CIOs' strategic responsibilities continue to evolve - Healthcare IT News**](https://www.healthcareitnews.com/news/health-system-cios-strategic-responsibilities-continue-evolve) | [[Healthcare IT News]] | | 11/7/2024 | [**Healthcare Compliance Professionals Grapple with Mounting Risks and Limited Resources**](https://hitconsultant.net/2024/11/08/healthcare-compliance-professionals-grapple-with-mounting-risks/) | [[HIT Consultant]] | | 9/6/2024 | [**Generative AI in Healthcare Market to Reach USD 19.99 Billion by 2032 Due to ... - WhaTech**](https://www.whatech.com/og/markets-research/medical/876879-generative-ai-in-healthcare-market-to-reach-usd-19-99-billion-by-2032-due-to-advancements-in-personalized-medicine-and-diagnostics.html) | whatech.com | | 9/3/2024 | [**Measuring the Impact of AI in Healthcare Virtual Summit On Demand**](https://www.healthitanswers.net/measuring-the-impact-of-ai-in-healthcare-virtual-summit-on-demand/) | [[Health IT Answers]] | | 7/10/2024 | [**Medical Billing Market Evolution: From USD 16.8 Billion to USD 27.7 Billion - MarketsandMarkets™**](https://www.prnewswire.co.uk/news-releases/medical-billing-market-evolution-from-usd-16-8-billion-to-usd-27-7-billion--marketsandmarkets-302193166.html) | prnewswire.co.uk | | 7/3/2024 | [**Healthcare Revenue Cycle Management Market Advancements Highlighted by Trend ... - WhaTech**](https://www.whatech.com/og/markets-research/it/851632-healthcare-revenue-cycle-management-market-trend-forecast-till-2032) | whatech.com | | 6/15/2024 | [**Responsible AI: Transforming risk management in the Philippines**](https://www.bworldonline.com/economy/2024/06/16/602169/responsible-ai-transforming-risk-management-in-the-philippines/) | bworldonline.com | ## Topic Overview (Some LLM-derived content — please confirm with above primary sources) ### Key Players - **ID Privacy AI**: A company focused on privacy AI technologies, addressing barriers to AI adoption in healthcare by providing secure deployment and management of AI systems. - **Upvio**: Conducting research on AI and technology adoption trends in healthcare. - **HIMSS**: A global advisor and thought leader in health information and technology, collaborating on AI adoption reports. - **Medscape**: A leading provider of medical news and education, involved in AI adoption research in healthcare. - **HiPaaS**: A company that facilitates responsible AI adoption in hospitals by providing interoperable solutions that integrate with existing EHR platforms. - **b.well Connected Health**: Developing a configurable AI architecture integrated into its FHIR-based platform to enhance healthcare ecosystems. - **Aidoc**: Collaborating with NVIDIA to develop the BRIDGE guideline for AI integration in healthcare. - **Qualified Health**: A startup focused on simplifying AI implementation and governance in healthcare. - **Suki**: A leader in AI technology for healthcare, known for its voice AI assistant that streamlines clinician workflows. - **Atropos Health**: Provides access to real-world data for healthcare AI developers, enabling better model training and validation. - **Intel**: A technology company involved in AI innovations and collaborations to enhance AI adoption. - **Vi**: An Enterprise-AI platform that provides a roadmap for organizations to enhance efficiency and improve patient outcomes through AI. - **ArisGlobal**: A company emphasizing the need for next-generation technology in Regulatory Affairs to ensure affordable patient access. - **Ferrum Health**: An AI platform that enhances healthcare operations by addressing security and scalability challenges, enabling AI integration into existing workflows. - **Define Ventures**: A firm that highlights the AI strategies of payers and providers amidst regulatory constraints and data quality issues. - **Keragon**: An AI-powered automation platform designed for the US healthcare industry, facilitating integration and efficiency. - **TetraScience**: A company focused on scientific AI, collaborating with Microsoft to enhance AI adoption in biopharmaceuticals. - **Microsoft**: A major technology company involved in partnerships to enhance AI capabilities across various sectors. - **Innovaccer Inc.**: A company that conducts research on AI's role in healthcare, focusing on operational efficiency and clinician burnout. - **Corti**: A company developing specialized foundation models for healthcare AI to enhance efficiency and reliability. ### Partnerships and Collaborations - **b.well Connected Health**: Launching an AI architecture to facilitate AI adoption in healthcare organizations. - **Aidoc and NVIDIA**: Developing guidelines to assist healthcare systems in AI integration. - **ISG**: Information Services Group conducts reports on global AI adoption, urging enterprises to invest in technology for competitiveness. - **TetraScience and Microsoft**: Collaborating to enhance scientific AI adoption across the biopharmaceutical value chain. - **Medscape and HIMSS**: Collaborated on a report detailing the current state and future expectations of AI in healthcare. - **Corti and YouGov**: Conducted a report highlighting the time healthcare professionals spend correcting AI outputs. - **EQTY Lab, Intel, and NVIDIA**: Launching the Verifiable Compute AI framework to improve AI governance and security. - **NVIDIA and AWS**: Collaborating to broaden access to AI technologies in healthcare, facilitating drug discovery and clinical trial processes. - **Informatica and Google Cloud**: Partnering to improve data management for analytics and generative AI applications. - **Suki and MEDITECH**: Partnered to integrate voice AI technology into healthcare systems, improving clinician efficiency. - **Ellipsis Health and Ceras Health**: A partnership aimed at improving mental health care using AI technology. - **Trialbee and Belong.Life**: Partnered to enhance clinical trial recruitment by connecting qualified patients with research opportunities. - **Mayo Clinic and Nvidia**: Collaborated to develop a Digital Pathology system utilizing a vast dataset for commercialization. - **Pennington Biomedical Research Center**: Collaborated on a study reviewing the evolution and challenges of electronic health records (EHRs) in medical research. - **Shanghai Sixth People's Hospital**: Partnered in research advocating for interdisciplinary partnerships to maximize EHR potential in advancing medical research. ### Innovations, Trends, and Initiatives - **AI Governance**: Increasing demand for AI governance tools to ensure compliance with regulations and mitigate risks associated with AI adoption. - **AI Training in Healthcare**: A significant skills gap exists, with only 24% of healthcare professionals receiving AI training from employers. - **AI in Clinical Trials**: Efforts to improve clinical trial recruitment and patient engagement through AI technologies. - **Generative AI Adoption**: Healthcare systems are increasingly adopting generative AI, but face governance and trust challenges. - **BRIDGE Guideline**: A framework to help healthcare organizations adopt and scale AI technologies effectively. - **AI Knowledge Market**: A federated data ecosystem introduced by ID Privacy AI to enhance enterprise AI capabilities while maintaining data privacy. - **Phased AI Implementation**: Healthcare organizations are adopting a phased approach to AI, starting with groundwork and moving towards comprehensive integration. - **AI in Healthcare**: AI is expected to enhance predictive analytics, personalized medicine, and clinical decision support, improving patient outcomes. - **Generative AI**: Rapid global adoption of generative AI is noted, with organizations leveraging it for operational efficiency and innovation. - **Real-World Data Access**: Atropos Health's GENEVA OS platform allows AI developers to train models on extensive patient data. - **HHS AI Strategic Plan**: Aims to enhance quality and outcomes in health services through responsible AI use. - **Generative AI in RCM**: Healthcare providers are exploring generative AI use cases to improve billing accuracy and reduce claim denials, despite facing integration challenges. - **Telehealth Expansion**: The COVID-19 pandemic accelerated the adoption of telehealth services, enhancing access to care but also presenting regulatory and privacy challenges. - **Specialized AI Models**: Corti's new AI models are built on healthcare data to improve accuracy and reduce the need for corrections. - **Verifiable Compute AI Framework**: A hardware-based solution designed to govern and audit AI workflows, enhancing explainability and security. - **Verifiable Compute AI framework**: A hardware-based solution designed to govern and audit AI workflows in real-time. - **Generative AI in Healthcare Market**: Projected to grow significantly, driven by demand for personalized treatment solutions. ### Challenges and Concerns - **Cultural Hesitations**: Reluctance among practitioners to adopt AI technologies due to operational challenges and data shortages. - **Lack of Expertise**: A significant barrier to AI adoption is the lack of in-house expertise and governance frameworks to manage AI technologies effectively. - **Data Quality and Integration**: Challenges in integrating legacy systems and ensuring data quality are significant barriers to AI adoption. - **Cost Barriers**: High costs of AI tools may exclude less-resourced health systems from benefiting from AI advancements. - **Lack of Trained Personnel**: A shortage of skilled professionals to implement and manage AI solutions hampers adoption efforts. - **Mistrust of Technology**: Healthcare professionals often exhibit mistrust towards AI technologies, which can hinder adoption and integration. - **Data Privacy and Security**: 43% of organizations cite data privacy concerns as a barrier to AI adoption, with healthcare facing unique challenges. - **Financial Limitations**: Healthcare organizations face significant financial barriers to adopting AI and automation technologies. - **Resistance to Change**: Healthcare professionals may resist adopting AI due to concerns about job displacement and the need for training to adapt to new technologies. - **Regulatory Compliance**: The heavily regulated nature of healthcare creates barriers to AI adoption, with organizations needing to navigate complex compliance requirements. - **Shortage of Trained Workforce**: A significant barrier to effective AI implementation in healthcare. - **Regulatory Challenges**: Strict regulations regarding patient data protection hinder AI adoption in healthcare. - **Financial Constraints**: Budgetary limitations hinder the adoption of AI technologies in healthcare settings. - **Regulatory Constraints**: Healthcare organizations struggle with complex regulations like HIPAA, which complicate AI implementation. - **Cost and Resource Allocation**: High costs associated with AI implementation and the need for significant financial investment pose challenges for many healthcare organizations. - **High Costs**: 37% of organizations report high maintenance costs and 33% face steep implementation costs as significant barriers. - **Integration Complexity**: Integrating AI with existing EHR systems is costly and complex, hindering the implementation of AI use cases in healthcare settings. - **Cybersecurity Threats**: Healthcare organizations face rising cybersecurity threats, complicating the adoption of AI technologies while ensuring compliance with regulations. ## Related Topics [[AI Adoption]]; [[AI Adoption in Healthcare]]; [[Adoption Barriers]]; [[AI Implementation Challenges]]; [[AI Implementation Risks]]