Healthcare Disparities & Global Oncology | Rudiment

The Challenge

Cancer outcomes vary dramatically based on factors such as geographic location, socioeconomic status, race, ethnicity, and access to healthcare. These disparities are driven by complex, interrelated factors: limited access to screening and early detection in underserved areas, delayed diagnosis and treatment initiation, variations in treatment quality and adherence, and social determinants of health that impact cancer risk and outcomes.

On a global scale, the challenge is even more profound, with approximately 70% of cancer deaths occurring in low- and middle-income countries (LMICs) where healthcare infrastructure, specialized oncology expertise, diagnostic capabilities, and treatment options may be severely limited. Traditional AI approaches often exacerbate these disparities by being developed with data from and for high-resource settings, potentially perpetuating or even amplifying existing inequities.

Our Approach

We aim to develop AI approaches specifically designed to address healthcare disparities and support global oncology efforts. Our multifaceted approach includes:

Creating AI models that perform effectively across diverse populations, actively addressing potential biases in training data and algorithms
Developing low-resource solutions that can function in settings with limited infrastructure, connectivity, computational power, or specialist expertise
Designing AI systems that transparently adapt to local contexts and resources, providing appropriate recommendations based on what's realistically available
Building tools that can help identify and quantify disparities in cancer care, supporting advocacy and policy efforts to address systemic inequities
Establishing collaborative frameworks that ensure AI development is community-engaged and reflects the needs and priorities of underserved populations

Equitable AI

Developing algorithms that perform consistently across diverse populations and actively mitigate biases in data and model predictions.

Resource-Adaptive Solutions

Creating AI tools that can function effectively in various resource settings, scaling their requirements and recommendations appropriately.

Access Enhancement

Building systems that extend specialist expertise to underserved areas through remote assessment, triage, and guidance for general healthcare providers.

Disparity Quantification

Leveraging AI to identify, measure, and track healthcare disparities across populations to inform targeted interventions and policy changes.

Potential Research Projects

Resource-Adaptive Cancer Diagnostic Platform

We could develop an AI system for cancer diagnosis that automatically adapts to the resources available in different settings. This platform might offer multiple tiers of functionality: in high-resource settings, it could integrate with advanced imaging and laboratory data; in mid-resource settings, it might rely on simpler imaging modalities and basic lab tests; and in low-resource settings, it could function with clinical photography, portable ultrasound, and minimal laboratory support. The system would transparently communicate its limitations while maximizing diagnostic performance with available inputs.

Bias Detection and Mitigation Framework

This project would focus on developing a comprehensive framework for identifying and addressing biases in oncology AI models. It would include methods for evaluating model performance across diverse subpopulations, techniques for augmenting underrepresented groups in training data, approaches for adjusting model outputs to ensure equitable performance, and transparency tools that clearly communicate when a model may be less reliable for certain populations due to training limitations.

Oncology Treatment Navigation for LMICs

We aim to create an AI-powered decision support system for cancer treatment in low- and middle-income countries that considers locally available resources, cost constraints, and practical implementation challenges. This system would provide evidence-based treatment recommendations tailored to what's realistically available, suggest adaptations to standard protocols when first-line options aren't available, and support task-shifting to extend specialist expertise through non-specialist providers where appropriate.

Healthcare Disparity Mapping and Intervention

This project would develop AI tools to identify and quantify disparities in cancer care access, quality, and outcomes across geographic and demographic dimensions. By integrating diverse data sources—including cancer registries, claims data, social determinants of health, and environmental factors—these tools would create detailed maps of cancer disparities. The system would then suggest targeted interventions based on the specific patterns and drivers identified, supporting evidence-based policy and resource allocation decisions.

Technical Innovations

Our healthcare disparities and global oncology research would leverage several cutting-edge technical approaches:

Transfer learning for low-resource settings: Techniques to adapt models trained on high-resource data to work effectively with limited data in diverse settings
Federated learning: Privacy-preserving approaches that enable model training across globally distributed data without centralizing sensitive patient information
Fairness-aware algorithms: Methods that explicitly optimize for equitable performance across different population groups
Lightweight AI deployment: Techniques for model compression and optimization to run sophisticated AI on limited computational resources
Explainable AI: Approaches that provide transparent explanations of model decisions, building trust and enabling adaptation to local contexts
Synthetic data generation: Methods to create diverse synthetic datasets that represent underrepresented populations for more inclusive model training

Application Areas

Our research would address multiple dimensions of healthcare disparities and global oncology:

Cancer Screening

Developing low-cost, accessible screening solutions that can detect cancer early in settings without advanced imaging or specialist expertise.

Diagnosis & Pathology

Creating diagnostic tools that can function with varying levels of pathology expertise and laboratory infrastructure, from full digital pathology to basic microscopy.

Treatment Planning

Building systems that recommend optimal treatment strategies based on locally available resources, balancing standard of care with practical implementation realities.

Regional Focus Areas

Our global oncology research would address specific challenges across different regions:

Sub-Saharan Africa

Addressing infectious agent-related cancers, late-stage presentation, and severe shortages in oncology workforce and treatment facilities.

South and Southeast Asia

Developing solutions for high population density areas with varied infrastructure, from metropolitan centers to remote rural communities.

Rural and Underserved Areas

Creating approaches that bridge geographic barriers to care through telehealth, remote consultation, and strategic resource allocation.

Indigenous Populations

Building culturally appropriate AI solutions that respect traditional health beliefs while improving cancer outcomes in indigenous communities.

Implementation Considerations

Successful implementation of global oncology AI requires attention to several key factors:

Connectivity challenges: Developing solutions that can function offline or with intermittent internet access
Infrastructure limitations: Creating tools that require minimal additional hardware or specialized equipment
Training and support: Building systems with intuitive interfaces and embedded training to minimize adoption barriers
Cultural contextualization: Ensuring solutions respect local health beliefs, practices, and communication norms
Sustainable deployment: Designing for long-term viability with consideration of maintenance, updates, and local ownership
Ethical considerations: Addressing data ownership, privacy, and consent issues in diverse regulatory environments

Future Directions

As our research progresses, we plan to explore several exciting directions:

Developing global collaboration networks for inclusive AI development with equitable participation from researchers worldwide
Creating specialized training programs to build AI expertise in underrepresented regions and communities
Exploring novel funding models that support development of AI solutions addressing health equity even when commercial incentives are limited
Building comprehensive policy frameworks for responsible deployment of AI in diverse global settings
Establishing shared data resources that appropriately represent global diversity for more inclusive AI development

Collaborations and Partnerships

We would be interested in exploring partnerships with:

Global Health Organizations

To ensure our solutions address priority cancer challenges in diverse global settings

Local Research Institutions

To co-develop solutions with teams who deeply understand regional challenges and opportunities

Health Equity Advocates

To ensure our research priorities and approaches align with the needs of underserved communities

AI for Healthcare Disparities & Global Oncology