# Bias in AI Models * **Definition:** Bias in AI models refers to systematic errors in predictions or decisions made by artificial intelligence systems that arise from prejudiced assumptions in the training data, algorithms, or model design, leading to unequal treatment or outcomes for different patient groups in healthcare applications. * **Taxonomy:** Healthcare Topics / Bias in AI Models ## News * Selected news on the topic of **Bias in AI Models**, for healthcare technology leaders * 2.4K news items are in the system for this topic * Posts have been filtered for tech and healthcare-related keywords | Date | Title | Source | | --- | --- | --- | | 4/29/2025 | [**Why Healthcare Organizations Must Prioritize AI Governance**](https://healthtechmagazine.net/article/2025/04/why-healthcare-organizations-must-prioritize-ai-governance) | [[HealthTech Magazine]] | | 4/8/2025 | [**How Multimodal AI Agents Could Transform Healthcare Relationships - LinkedIn**](https://www.linkedin.com/pulse/how-multimodal-ai-agents-could-transform-healthcare-leo-barella-lst6f) | [[Linkedin]] | | 3/27/2025 | [**The AI Prescription: The Risks and Responsible Use of AI in Healthcare Technology**](https://www.healthcareittoday.com/2025/03/27/the-ai-prescription-the-risks-and-responsible-use-of-ai-in-healthcare-technology/) | [[Healthcare IT Today]] | | 3/21/2025 | [**AI Governance & Compliance Framework for LLMs in Healthcare - Medium**](https://medium.com/@dr.davuluri/ai-governance-compliance-framework-for-llms-in-healthcare-52ec6d7bffef) | [[Medium]] | | 3/9/2025 | [**Can AI Cure Healthcare Faster Than It Creates New Ethical Wounds? - LinkedIn**](https://www.linkedin.com/pulse/can-ai-cure-healthcare-faster-than-creates-new-wounds-mekonnen-md--kwxhf) | [[Linkedin]] | | 3/9/2025 | [**AI in Healthcare: Minimum Requirements to Enter the Field - Medium**](https://medium.com/@hiya31/ai-in-healthcare-minimum-requirements-to-enter-the-field-eb76bf824895) | [[Medium]] | | 3/2/2025 | [**Can AI Outperform Doctors in Diagnosing Infectious Diseases?**](https://www.news-medical.net/health/Can-AI-Outperform-Doctors-in-Diagnosing-Infectious-Diseases.aspx) | [[News Medical Net]] | | 2/27/2025 | [**The Promise and Pitfalls of AI in Disease Progression Monitoring - by Keerthi - Medium**](https://medium.com/@keerthimindnotix/the-promise-and-pitfalls-of-ai-in-disease-progression-monitoring-fe3a7b891584) | [[Medium]] | | 2/11/2025 | [**AI-Powered Healthcare: Breakthroughs, Policies & Future Trends - Raouf Hajji, MD, PhD.**](https://www.linkedin.com/pulse/ai-powered-healthcare-breakthroughs-policies-future-hajji-md-phd--oaanf) | [[Linkedin]] | | 2/10/2025 | [**Launching the Trustworthy and Responsible AI Network (TRAIN) A Consortium to Facilitate ...**](https://jamanetwork.com/journals/jama/articlepdf/2830340/jama_emb_2025_vp_250019_1738942291.693.pdf) | [[JAMA Network]] | | 1/30/2025 | [**Healthcare AI Agents: New Opportunities for the High-Tech Industry - Medium**](https://medium.com/@alexglee/healthcare-ai-agents-new-opportunities-for-the-high-tech-industry-f19c6c41c232) | [[Medium]] | | 1/27/2025 | [**How Does Retrieval-Augmented Generation (RAG) Support Healthcare AI Initiatives?**](https://healthtechmagazine.net/article/2025/01/retrieval-augmented-generation-support-healthcare-ai-perfcon) | [[HealthTech Magazine]] | | 1/25/2025 | [**Environment scan of generative AI infrastructure for clinical and translational science**](https://www.nature.com/articles/s44401-024-00009-w) | [[Nature]] | | 12/20/2024 | [**The Download: Digital Twins, AI Data, and the Future of Healthcare - Medium**](https://medium.com/vanguard-industry-foresight/the-download-digital-twins-ai-data-and-the-future-of-healthcare-64cb675dfb58) | [[Medium]] | | 12/15/2024 | [**The Future of AI in Drug Development - by Kayden Break - Operations Research Bit**](https://medium.com/operations-research-bit/the-future-of-ai-in-drug-development-463121a2e30d) | [[Medium]] | | 12/4/2024 | [**Artificial intelligence tops 2025 health technology hazards list - PR Newswire**](https://www.prnewswire.com/news-releases/artificial-intelligence-tops-2025-health-technology-hazards-list-302322748.html) | [[PR Newswire]] | | 10/30/2024 | [**Security and Data Sharing: Q&A with The Pistoia's Alliance's Dr. Becky Upton**](https://www.pharmexec.com/view/security-data-pistoia-alliance-becky-upton) | [[PharmExec]] | | 10/27/2024 | [**Civitas 2024: Advancing Health Data Exchange through Local Partnerships and Data Integration**](https://www.healthitanswers.net/civitas-2024-advancing-health-data-exchange-through-local-partnerships-and-data-integration/) | [[Health IT Answers]] | | 9/25/2024 | [**How AI is shaping the future of medicine - Fast Company**](https://www.fastcompany.com/91196757/how-ai-is-shaping-the-future-of-medicine) | [[Fast Company]] | | 9/14/2024 | [**Weekly Roundup - September 14, 2024 - Healthcare IT Today**](https://www.healthcareittoday.com/2024/09/14/weekly-roundup-september-14-2024/) | [[Healthcare IT Today]] | | 8/13/2024 | [**5 Key Providers Taking Precision Medicine into the Cloud**](https://www.insideprecisionmedicine.com/topics/precision-medicine/5-key-providers-taking-precision-medicine-into-the-cloud/) | [[Inside Precision Medicine]] | | 7/22/2024 | [**Data Science in Healthcare: Innovations and Challenges**](https://www.thequint.com/brandstudio/partner-data-science-healthcare) | thequint.com | | 7/17/2024 | [**Seneca Polytechnic Leads Seminar on AI In Healthcare, Announces New Programs And Industry Collaborations**](https://www.bignewsnetwork.com/news/274460794/seneca-polytechnic-leads-seminar-on-ai-in-healthcare-announces-new-programs-and-industry-collaborations) | bignewsnetwork.com | | 7/16/2024 | [**Ethical Considerations in the Use of Artificial Intelligence and Machine Learning in Health Care**](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11249277/) | [[NCBI - NIH]] | | 7/11/2024 | [**Seven Important Actions to Manage Cyber Risk While Benefiting from AI**](https://www.healthitanswers.net/seven-important-actions-to-manage-cyber-risk-while-benefiting-from-ai/) | [[Health IT Answers]] | ## Topic Overview (Some LLM-derived content — please confirm with above primary sources) ### Key Players - **SolasAI**: Recognized for its commitment to fair and responsible AI practices, aiming to correct biases in AI models. - **OpenAI**: A leading AI research organization known for developing advanced language models like GPT-3 and GPT-4, which are used in various applications including healthcare. - **ECRI Institute**: Identifying AI in healthcare as a top technology hazard, emphasizing the need for proper management. - **Coalition for Health AI**: A collaborative organization focused on establishing best practices and definitions for responsible AI in healthcare. - **PointClickCare**: A healthcare technology company focusing on AI solutions that prioritize trustworthiness and transparency in AI models. - **UChicago Medicine**: A health system exploring AI to address healthcare challenges while ensuring equitable deployment and monitoring of AI technologies. - **Google Cloud**: A tech leader predicting the evolution of AI applications in healthcare. - **Trustworthy and Responsible AI Network (TRAIN)**: A consortium aimed at promoting safe and effective adoption of AI in various sectors, including healthcare. - **FDA**: Implementing oversight measures for healthcare AI to balance innovation with patient safety. - **Epic**: Focusing on reducing costs associated with AI in their healthcare products. - **Mpathic**: Focuses on improving cultural attunement in mental health care through AI tools. - **World Health Organization**: An international organization emphasizing the importance of governance frameworks for AI technologies in healthcare. - **DeepMind**: A leader in AI research, known for developing AlphaFold, which enhances diagnostic accuracy in healthcare. - **Deloitte**: A consulting firm that conducts surveys and research on AI adoption in organizations, highlighting governance and risk management issues. - **IBM Watson Health**: Utilizes AI to match cancer patients with suitable clinical trials, enhancing personalized medicine. - **American College of Radiology**: Initiated an AI quality assurance program focusing on governance, algorithm documentation, security, compliance, and performance monitoring. - **Microsoft**: Introduced new AI capabilities for healthcare organizations, including Azure AI Studio and Copilot Studio, aimed at improving patient care and operational efficiency. - **ECRI**: An organization that provides evidence-based research and recommendations for healthcare technology, emphasizing the importance of AI safety and transparency. ### Partnerships and Collaborations - **Cross-Industry Collaborations**: Aimed at creating best practices for ethical AI deployment and addressing bias. - **International Cooperation**: Emphasized by experts for AI governance to address complexities and biases in AI systems. - **CAIRT**: The Crowdsourced Artificial Intelligence Red-Teaming Assurance Program, a collaboration between the U.S. Department of Defense and Humane Intelligence, evaluates large language models for military medical services. - **TRAIN Consortium**: Fosters collaboration among healthcare professionals, researchers, and policymakers to address AI challenges and promote ethical practices. - **ALIGNMT AI and HFMA**: Launched a micro-credentialing program to enhance AI governance skills among healthcare professionals. - **GE HealthCare and AWS**: Partnering to develop generative AI models aimed at improving clinical workflows and patient care. - **UChicago Medicine and AI Researchers**: Collaboration to enhance digital health and AI integration, focusing on digital equity for diverse patient populations. - **e& and IBM**: Collaborating to implement a comprehensive AI governance solution aimed at enhancing compliance and ethical practices. - **Mayo Clinic and Nvidia**: Collaborating to enhance the performance and scalability of generative AI in pathology. - **Avant Technologies, Inc. and Ainnova Tech, Inc.**: A joint venture focused on advancing early disease detection using AI technologies. - **UK's National Health Service**: Leveraging AI to identify patients at risk of high emergency service usage. - **Thrive AI Health**: Collaborates with Stanford Medicine to develop an AI health coach for personalized healthcare. - **Public Consulting Group and Synergist Technology**: Formed a strategic partnership to enhance AI governance, security, and compliance solutions across various industries. - **Dynex and InCor**: Working together to enhance the use of large language models in healthcare through neuromorphic quantum computing. - **Seneca Polytechnic**: Hosted seminars and announced new degree programs to equip students with AI skills relevant to healthcare. - **Mpathic and Wave**: Working together to analyze conversational data to enhance cultural responsiveness in mental health care. - **HEALIX**: Developed in collaboration with Amazon Web Services, HEALIX is a cloud-based analytics platform for Singapore's public healthcare sector, aimed at standardizing and securing analytics projects. ### Innovations, Trends, and Initiatives - **AI Governance Frameworks**: Becoming essential for ensuring fairness, transparency, and alignment with societal values in AI development. - **AI Ethics Framework**: Proposed by NIH to address biases and ensure accountability in AI applications in healthcare. - **Generative AI**: Transforming healthcare by creating synthetic datasets and improving diagnostics while raising concerns about bias. - **AI in Diagnostics**: AI tools are improving diagnostic accuracy, such as Google's AI model for mammography that reduces false positives and negatives. - **AI Ethics Committees**: Recommended for organizations to oversee AI projects and ensure adherence to ethical standards. - **AI Risk Management Framework**: A resource for organizations to develop policies promoting responsible AI development and mitigate compliance risks. - **AI Governance Market**: Projected growth driven by the need for ethical AI use and regulatory compliance. - **Explainable AI**: An emerging trend aimed at improving transparency in AI algorithms to ensure trust and understanding in healthcare applications. - **AI Explainability**: Growing emphasis on making AI decision-making processes understandable to ensure trust and safety in healthcare applications. - **Generative AI in Healthcare**: Increasing adoption of generative AI models for improving diagnostics, operational efficiency, and personalized treatment plans. - **AI in Drug Approval**: AI is enhancing drug approval processes by improving study design and patient engagement. - **Decentralized Clinical Trials**: AI is optimizing recruitment and analyzing trial data to expedite regulatory approvals. - **AI Quality Assurance Program**: The American College of Radiology's initiative to ensure the quality and safety of AI applications in radiology. - **Homomorphic Encryption**: A recent study in South Korea demonstrated that homomorphic encryption can enhance AI model predictive capabilities while safeguarding patient data privacy. - **Project Digits**: Nvidia's mini AI supercomputer that democratizes access to supercomputer-grade AI technology, enabling small enterprises to develop advanced AI models. - **Clinical Decision Support Systems**: AI is personalizing clinical decision support systems to improve patient outcomes. - **National AI Strategy 2.0**: Singapore's initiative to guide public and private sectors in leveraging AI for healthcare advancements. ### Challenges and Concerns - **Bias in AI Models**: AI models can perpetuate biases leading to discriminatory practices, particularly in healthcare, necessitating diverse datasets and fairness testing. - **Bias in AI Systems**: A significant concern as it can exacerbate healthcare disparities, necessitating solutions that prioritize fairness. - **Algorithmic Bias**: Bias in AI models can exacerbate healthcare disparities and impact the quality of care provided to patients. - **Data Quality and Diversity**: Concerns about the accuracy of AI models due to reliance on the quality and diversity of training data, which can lead to biased outcomes affecting patient care. - **Data Quality**: Poor data quality can lead to biased decision-making in AI applications. - **Training Data Diversity**: The importance of inclusive training practices to mitigate risks of algorithmic discrimination. - **Trust and Accountability**: The 'black box' nature of AI complicates validation of AI-generated insights, raising concerns about trust among healthcare providers. - **Equity in AI Governance**: A significant lack of consistent equity consideration in the governance of predictive technologies highlights the need for health systems to prioritize equity literacy. - **Model Interpretability**: The complexity of AI models makes it difficult for healthcare professionals to understand decision-making processes, leading to potential errors. - **Privacy Concerns**: Data privacy issues must be addressed to ensure the ethical use of AI in healthcare. - **Data Privacy and Security**: Concerns regarding the handling of personal data in AI systems, leading to fears of breaches and unauthorized surveillance. - **Data Privacy and Compliance**: Concerns regarding adherence to regulations like GDPR and HIPAA, especially in the context of AI decision-making. - **Output Reliability**: Fears regarding the reliability of AI outputs, stressing the importance of accuracy to prevent risks to patient safety. - **Lack of Oversight Frameworks**: Current absence of oversight for AI integration into healthcare systems raises patient safety and ethical concerns. - **Regulatory Hurdles**: The lack of established mechanisms for ensuring AI safety and effectiveness in healthcare settings. ## Related Topics [[Bias in AI]]; [[Bias in AI Algorithms]]; [[Bias in AI Systems]]; [[Bias in AI Training Data]]; [[Algorithmic Bias]]