# Algorithmic Bias * **Definition:** Algorithmic Bias in healthcare refers to systematic and unfair discrimination that occurs when algorithms used in medical decision-making, diagnostics, or treatment recommendations produce results that favor certain groups over others, often due to biased training data, flawed model design, or socio-economic disparities. * **Taxonomy:** Healthcare Topics / Algorithmic Bias ## News * Selected news on the topic of **Algorithmic Bias**, for healthcare technology leaders * 1.7K news items are in the system for this topic * Posts have been filtered for tech and healthcare-related keywords | Date | Title | Source | | --- | --- | --- | | 5/26/2025 | [**AI can solve many gaps in healthcare, but only with ethical implementation - Viewpoint**](https://www.chiefhealthcareexecutive.com/view/ai-can-solve-many-gaps-in-healthcare-but-only-with-ethical-implementation-viewpoint) | [[Chief Healthcare Executive]] | | 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]] | | 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]] | | 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]] | | 2/3/2025 | [**AI Agents: New Frontier for Transforming Healthcare - Alex G. Lee, Ph.D. Esq. CLP**](https://www.linkedin.com/pulse/ai-agents-new-frontier-transforming-healthcare-lee-ph-d-esq-clp-5yfbe) | [[Linkedin]] | | 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]] | | 1/25/2025 | [**Environment scan of generative AI infrastructure for clinical and translational science**](https://www.nature.com/articles/s44401-024-00009-w) | [[Nature]] | | 12/31/2024 | [**AI Regulations: A Fragmented but Global Necessity in the Digital Healthcare Era - LinkedIn**](https://www.linkedin.com/pulse/ai-regulations-fragmented-global-necessity-digital-healthcare-ahmed-raure) | [[Linkedin]] | | 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]] | | 12/18/2024 | [**Tackling algorithmic bias and promoting transparency in health datasets - The Lancet**](https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00224-3/fulltext?rss=yes) | [[The Lancet]] | | 12/18/2024 | [**Tackling algorithmic bias and promoting transparency in health datasets - The Lancet**](https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00224-3/fulltext) | [[The Lancet]] | | 12/13/2024 | [**AI Innovations in Healthcare Technology - by David Hirschfeld - Dec, 2024 - Medium**](https://medium.com/@dmhirschfeld/ai-innovations-in-healthcare-technology-6873f6cf1dfc) | [[Medium]] | | 12/2/2024 | [**2024 Tech And IT Recap: Transformations, Trials, And Triumphs - Forbes**](https://www.forbes.com/sites/emilsayegh/2024/12/02/2024-tech-and-it-recap-transformations-trials-and-triumphs/) | [[Forbes]] | | 11/22/2024 | [**Health Boards Of Directors Must Drive AI Governance And Accountability - Forbes**](https://www.forbes.com/councils/forbestechcouncil/2024/11/22/health-boards-of-directors-must-drive-ai-governance-and-accountability/) | [[Forbes]] | | 11/12/2024 | [**Global Artificial Intelligence (AI) Influence on Healthcare Market Expected to Reach $148 ...**](https://www.morningstar.com/news/globe-newswire/9271751/global-artificial-intelligence-ai-influence-on-healthcare-market-expected-to-reach-148-billion-by-2029) | [[Morningstar]] | | 10/7/2024 | [**AI-Powered Scientific Discovery in Healthcare - by Anatole Martins - Oct, 2024 - Medium**](https://medium.com/@anatole.martins6730/ai-powered-scientific-discovery-in-healthcare-6b1620256fd9) | [[Medium]] | | 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]] | | 9/2/2024 | [**Revolutionising healthcare: Big data analytics and AI at the forefront of medical innovation**](https://gulfnews.com/uae/health/revolutionising-healthcare-big-data-analytics-and-ai-at-the-forefront-of-medical-innovation-1.1725260556563) | gulfnews.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]] | | 7/3/2024 | [**Transformative AI Applications in Healthcare: Enhancing Diagnostics, Treatment, and Efficiency**](https://www.techtimes.com/articles/306332/20240703/transformative-ai-applications-in-healthcare-enhancing-diagnostics-treatment-and-efficiency.htm) | techtimes.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 - **Microsoft**: Developing AI-powered healthcare solutions to enhance patient care and address algorithmic bias. - **Maria May**: An expert addressing ethical challenges associated with AI in healthcare, including algorithmic bias. - **Nanyang Polytechnic (NYP) and AI Singapore (AISG)**: Educational institutions collaborating to create AI training programs that address ethical considerations, including algorithmic bias. - **Brookings Center for Technology Innovation**: Established The AI Equity Lab to promote responsible and inclusive AI design across various sectors, including healthcare. - **FDA**: The U.S. Food and Drug Administration, which oversees the approval of AI and machine learning-driven medical devices and emphasizes the need for transparency in algorithm performance. - **Health and AI Working Group**: A group of 14 experts focused on ensuring AI applications in healthcare are inclusive for underrepresented and medically vulnerable communities. - **ECRI**: An organization highlighting the risks of AI in healthcare and advocating for safety and transparency. - **Dana-Farber Cancer Institute**: Developed a generative AI tool for interpreting lab results, showcasing practical applications of AI in healthcare. - **Genomics England**: Conducted research identifying genetic links to intellectual disabilities, demonstrating AI's potential in medical research. - **OpenAI**: A leading AI research organization known for developing advanced AI models, including GPT-4o, which has implications for healthcare technology. - **World Health Organization**: Emphasizes the importance of international collaboration and governance frameworks to ensure AI technologies meet the needs of diverse populations. - **DLA Piper**: A multinational law firm integrating data scientists to ensure compliance with AI regulations. - **Peterson Health Technology Institute**: An organization that has initiated an AI task force to evaluate the impact of AI on healthcare costs and efficiency. - **American College of Physicians**: Published policy papers on the ethical use of AI in healthcare, emphasizing the importance of physician involvement. - **Brian Anderson**: CEO of the Coalition for Health AI, advocating for collaboration between AI companies and health systems. - **Mayo Clinic**: A prominent healthcare institution that is leveraging AI technologies, including a Digital Pathology system, to enhance patient care and diagnostics. - **Partha Anbil**: A leader in healthcare technology emphasizing the importance of open-source software models for transparency and collaboration. - **NIBIB**: The National Institute of Biomedical Imaging and Bioengineering focuses on expanding the scientific workforce and developing accessible technologies for all populations. ### Partnerships and Collaborations - **STANDING Together**: An initiative that developed recommendations to address algorithmic biases in AI health technologies, involving input from over 350 representatives across 58 countries. - **AI Equity Lab**: Collaborates with various sectors, including healthcare and education, to promote inclusive AI design. - **U.S. Department of Defense and Humane Intelligence**: Conducting a red-teaming pilot program to evaluate biases in AI models for military medical services. - **Coalition for Health AI**: Working with various organizations to create standardized approaches for AI development and implementation in healthcare. - **WHO and community advocates**: Collaboration to create regulatory frameworks that ensure ethical AI use in healthcare. - **Texas Responsible AI Governance Act (TRAIGA)**: A proposed framework for responsible AI use in Texas, aiming to establish guidelines for ethical AI deployment. - **Tata Consultancy Services**: Exploring federated learning technologies to utilize real-world data from wearables for AI model training while ensuring data privacy. - **Mayo Clinic and Nvidia**: Collaboration to develop a Digital Pathology system that utilizes AI for improved diagnostics. - **NYP and AISG**: Collaboration to launch the Certified AI Practitioners for Design & Media certification, integrating AI training into educational curricula. - **HBCU BEITA program**: Aims to enhance bioengineering and imaging research capacity at Historically Black Colleges and Universities to meet the growing need for biomedical innovation. - **Mpathic and Wave**: Collaborating to analyze conversational data to improve cultural responsiveness in mental health care. - **Thumos Care and Public-Private Partnerships**: Thumos Care's proposed legislation includes support for public-private partnerships to address health disparities through AI-enhanced services. - **Avant Technologies, Inc. and Ainnova Tech, Inc.**: Formed a joint venture to advance early disease detection using AI, focusing on commercializing Ainnova's technology portfolio. - **Florida State University College of Nursing**: Introduced the nation's first AI-focused Master of Science in Nursing program to enhance nursing education and prepare future healthcare workers. ### Innovations, Trends, and Initiatives - **AI Governance Programs**: Healthcare organizations are establishing robust AI governance frameworks to address algorithmic bias and ensure ethical AI deployment. - **AI and Machine Learning in Healthcare**: Growing integration of AI technologies in healthcare, with a focus on improving patient outcomes while addressing algorithmic bias. - **Digital Twins**: Utilizing digital twins of human organs for surgical practice, requiring vast amounts of data and addressing algorithmic bias. - **AI in Mental Health**: AI tools are being integrated into mental health care to improve accessibility and diagnostic accuracy, while also raising concerns about algorithmic bias. - **Generative AI**: Set to revolutionize healthcare by enhancing diagnostics and treatment while addressing algorithmic bias and data privacy. - **STANDING Together Recommendations**: Focus on documenting health datasets to ensure transparency and mitigate algorithmic biases, promoting equitable access to healthcare technologies. - **AI Safety Frameworks**: Current frameworks criticized for being Western-centric, highlighting the need for inclusive approaches to AI safety. - **AI-Enhanced Universal Health and Prevention Act of 2024**: Proposed legislation aimed at providing equitable access to AI-enhanced preventive health services, including the establishment of an AI Fairness Board. - **AI Governance Frameworks**: Recommendations for comprehensive AI governance frameworks, such as IEEE UL 2933 and NIST AI RMF, to ensure ethical AI deployment in healthcare. - **AI in Medical Imaging**: AI algorithms are being utilized to improve breast cancer detection and diabetic retinopathy identification, significantly enhancing early diagnosis. - **AI in Diagnostics**: AI technologies are being integrated into healthcare for enhanced diagnostic accuracy and operational efficiencies, including predictive analytics and personalized medicine. - **AI-Powered Diagnostics**: AI tools improving accuracy in disease detection, such as a UCLA study showing AI's 84% accuracy in detecting prostate cancer. - **AI Governance Alliance**: Aims to ensure AI enhances human capabilities and promotes inclusive growth through a comprehensive roadmap addressing governance challenges. - **EU AI Act**: Establishes a regulatory framework for AI, focusing on compliance and ethical standards. - **Generative AI Applications**: Tools like those developed by Dana-Farber and Genomics England illustrate the transformative potential of AI in enhancing patient outcomes. - **Federated Learning**: A technology being explored to allow hospitals to use real-world data for AI training without compromising privacy. - **Generative AI Tools**: Mpathic's generative AI tools aim to enhance cultural attunement in mental health care. ### Challenges and Concerns - **Algorithmic Bias**: A significant issue in AI applications that can perpetuate health disparities, necessitating strategies for inclusive technology design. - **Transparency**: The challenge of understanding AI decision-making processes, which can lead to errors if based on biased data. - **Lack of Equity Consideration**: A study revealed that many healthcare leaders do not adequately address issues of inequity and bias in AI governance frameworks. - **Regulatory Challenges**: The need for standardized data reporting from regulatory bodies like the FDA to evaluate algorithm performance across diverse populations. - **Data Privacy and Security**: Concerns regarding data privacy, HIPAA compliance, and algorithm interpretability must be addressed to ensure safe and effective implementation of AI. - **Data Privacy**: Concerns regarding the use of patient data without consent and the need for transparency in AI applications. - **Ethical Considerations**: The need for clear ethical guidelines and accountability frameworks to mitigate risks associated with AI deployment in healthcare. - **Lack of Oversight**: The absence of regulatory frameworks for AI integration in healthcare raises significant ethical and operational concerns. - **Overreliance on Technology**: The potential for AI to undermine human connection in healthcare, particularly in mental health services. - **Trust and Acceptance**: Resistance from healthcare professionals regarding AI adoption due to fears of job displacement and ethical implications. - **Skepticism Among Healthcare Professionals**: Despite the potential benefits of AI, many healthcare workers express reluctance and skepticism towards its use, raising concerns about its integration. - **Privacy and Data Security**: Increased concerns about privacy and data security as AI systems become more adept at collecting and analyzing personal data. ## Related Topics [[Algorithm Bias]]; [[Data Privacy and Algorithmic Bias]]; [[Algorithm Biases]]; [[Bias in AI Algorithms]]; [[Bias in AI Systems]]; [[Bias in AI]]; [[Bias in AI Models]]