zigzag
A cross-disciplinary research initiative focused on ethical AI implementations in healthcare, ensuring that artificial intelligence enhances patient care while addressing critical ethical challenges.
Collaborators
zigzag
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Overview
Ethical AI in healthcare
The zigzag project explores the complex intersection of artificial intelligence, ethics, and healthcare delivery. Our cross-disciplinary team brings together experts in medicine, AI research, ethics, patient advocacy, and policy to ensure that AI implementations in healthcare settings are effective, fair, transparent, and centered on improving patient outcomes.
Named for the non-linear path that ethical innovation often follows, zigzag acknowledges that progress in healthcare AI requires careful navigation between technical possibilities and ethical imperatives.
Key Challenges We Address
- Algorithmic bias in clinical decision support systems
- Privacy and data protection in healthcare AI applications
- Transparency and explainability of diagnostic AI
- Equitable access to AI-enhanced healthcare
- Balancing automation with human medical expertise
- Regulatory frameworks for healthcare AI applications
Core Principles
- Patient-centered: Prioritizing patient well-being and autonomy
- Inclusive: Ensuring AI systems work for diverse populations
- Transparent: Making AI decision-making processes understandable
- Evidence-based: Rigorously testing AI systems for safety and efficacy
- Accessible: Promoting equitable distribution of AI benefits
- Accountable: Clear responsibility frameworks for AI implementations
"The zigzag project represents a crucial step toward ensuring that AI in healthcare serves humanity's needs while respecting fundamental ethical principles and human dignity." — Dr. Maya Hernandez, Bioethics Director at Health AI Institute
Research
Our investigative focus
Our research methodology combines technical AI development with ethical frameworks, clinical expertise, and patient perspectives to create a holistic approach to healthcare AI.
Research Streams
Fairness in Clinical AI
Investigating methods to detect and mitigate bias in clinical prediction algorithms across diverse patient populations.
Explainable Diagnostic AI
Developing techniques that make complex diagnostic AI systems understandable to clinicians and patients.
Privacy-Preserving Healthcare ML
Creating machine learning frameworks that maintain patient privacy while enabling beneficial uses of health data.
Human-AI Collaboration
Studying optimal models for collaboration between healthcare professionals and AI systems.
Key Publications
"Ethical Frameworks for Clinical AI Implementation: A Multi-stakeholder Approach"
Journal of Medical Ethics, 2024
Peterson, J., Kim, S., Okonkwo, C., et al.
"Detecting and Mitigating Bias in Cardiovascular Risk Prediction Algorithms"
Nature Machine Intelligence, 2023
Martinez, L., Suzuki, T., O'Neill, B., et al.
"Patient Perspectives on AI in Clinical Care: A Qualitative Analysis"
BMJ Health & Care Informatics, 2023
Wong, A., Patel, R., Mendez, J., et al.
Applications
Ethical AI in action
Our research directly translates into practical applications and frameworks that shape how AI is designed, implemented, and regulated in healthcare settings.
FairDiagnosis
An open-source framework for evaluating and improving fairness in diagnostic AI systems across diverse patient populations.
Deployed in 8 healthcare systems for validation
Technical DocumentationEthical AI Audit Toolkit
A comprehensive methodology for healthcare organizations to assess AI systems against ethical principles and regulatory requirements.
Adopted by healthcare regulators in 3 countries
Download ResourcesPatientUnderstand
Tools for communicating AI-generated healthcare insights to patients in accessible, understandable ways.
Tested with 1,200+ patients across diverse backgrounds
Case StudiesPrivacy-First ML Pipeline
A secure infrastructure for healthcare institutions to collaborate on AI development while protecting patient privacy.
Enabling research across 12 institutions without data sharing
Technical PaperClinical AI Policy Framework
Guidelines and templates for institutions developing governance policies for clinical AI implementation.
Referenced in national healthcare AI strategies
View FrameworkAI Ethics Training
Comprehensive educational program for healthcare professionals on ethical use of AI in clinical settings.
Over 5,000 clinicians trained worldwide
Course InformationCollaborators
Our multidisciplinary team
zigzag brings together a diverse team of experts from across disciplines to ensure a comprehensive approach to ethical AI in healthcare.
Core Partners
University Medical Center
Clinical expertise and real-world healthcare perspectives
Ethics in Technology Institute
Ethical frameworks and governance models
Patient Advocacy Coalition
Patient perspectives and inclusive design
Medical AI Research Lab
Technical expertise in healthcare AI development
Healthcare Policy Institute
Regulatory perspectives and policy recommendations
Global Health Equity Foundation
Focus on health disparities and global access
Join Our Collaboration
We're always looking for new partners who share our commitment to ethical AI implementation in healthcare.