Sanitas AI

More than 1 in 5 Natives reported discrimination in clinical encounters, and 15% of Natives have avoided seeking medical care due to anticipated discrimination. We seek to get to the root of the problem─ research and the ways it can be streamlined directly into healthcare and provide better training and patient outcomes. 

We want to transform and change the ways we work with data through innovation and traditional knowledge systems. Modern technology do not focus on the ways they can impact communities, and its peoples.They are prone to bias, especially within the medical field. With the future of AI, it is pertinent that advancements in medicine do not cause further inequality and inequity.

Our solution is a platform for medical researchers to manage, annotate, and analyze their data. We incorporate machine learning to automate the process of annotating and managing data, and are integrating generative AI to help researchers increase data throughput and generate new data. 

The ways we use Indigenous knowledge systems in our work, is (1) community: connecting researchers more directly through our platform, allowing for easier collaboration and input to better their work and data analysis, (2) consider all people: not everyone is technically-minded nor needs to be, researchers do not have access to easy-to-use technologies that work to serve them and their needs, (3) indigenous perspective in AI and data: we ask what are the ways AI is biased in the medical space? we seek to answer these questions thorough a careful examination of the biases that occur within this space and applications of traditional knowledge systems. 

Our solution is primarily (first-order) is for universities and hospitals that do medical research. The second-order impact is on the Indigenous community at large. Medical discrimination rampantly affects the Indigenous community and not to mention, there is much inherit bias within medical machine learning models. If Native affiliated universities, hospitals, and researchers had access to software that enabled to do research on specific populations, then that would go down. In short, with more Indigenous data and medical research, more Indigenous lives would be better off.

The Indigenous communities this would primarily benefit would be those that have access to hospitals, universities, or clinics that are tribal affiliated or that work with us.

As the team-lead, I have always been involved within community. In community college, I worked as a tutor that helped Indigenous, LatinX and black high-school students in their academics, as well as applying to colleges, teaching them about the possibilities of a career and educated them on financing their journeys in safe ways.

When I transferred to UC Berkeley, I became involved in the Native outreach programs and connected with prospective students and helped serve as a mentor. 

Recently, I have been very involved in AISES which has not only connected me to a network of Indigenous peoples, but also has taught me many invaluable skills of how to better serve these communities through workshops, sessions and talking circles.

We want to connect and learn from a like-minded community. Community is very important for Indigenous people and we believe that the best way to transform and work on the problems we wish to solve, is to have a network of support from others who believe in the same things. 

We want to be more than a startup in the tech industry that just builds a product and has customers.

As the team lead, I have been very involved in the AISES(American Indian STEM Society) and was selected for their STEM business cohort that helps Indigenous entrepreneurs learn about applying Indigenous knowledges within the field and develops skills in tribal law and government, Indigenous rights, and education. 

I have participated in various talking circles and have connected with many amazing individuals within the community. 

I am also part of my schools, UC Berkeley's, Native outreach program and have participated in many of their events. 

1. Comprehensive integration: Unlike existing platforms, our solution not only incorporates AI-driven analytics but also deeply embeds traditional indigenous knowledge systems. This allows researchers to access a more diverse and holistic set of insights, which can lead to innovative and culturally relevant outcomes.

2. Raising awareness: By demonstrating the value of integrating indigenous knowledge systems into research, our platform can inspire other researchers and institutions to explore similar approaches, fostering a more inclusive and diverse research landscape.

3. Empowering indigenous communities: By involving indigenous communities in the development process and acknowledging their intellectual property rights, our platform can empower these communities, enabling them to participate in and benefit from research outcomes

4. Shifting research paradigms: Our platform could prompt a shift in research methodologies, encouraging more researchers to embrace the integration of indigenous knowledge and AI-driven analytics, leading to more comprehensive and nuanced research outcomes.

5. Setting ethical standards: By promoting ethical practices and guidelines for the integration of indigenous knowledge, our platform could help establish industry-wide standards that protect indigenous communities from exploitation and appropriation, ensuring that their knowledge is respected and valued in the research process

6. Expanding market opportunities: The growing interest in combining AI and traditional knowledge systems could drive the demand for similar platforms, tools, and services, creating new market opportunities for technology providers, indigenous communities, and researchers.

1. Short-term goals: Finalize platform development, establish partnerships, and train users.
2. Long-term goals: Expand platform adoption, scale impact, influence policy, and innovate continuously.
3. Engage communities: Maintain ongoing dialogue with indigenous communities and researchers.
4. Leverage partnerships: Collaborate with stakeholders in academia, government, and indigenous organizations.
5. Monitor impact: Regularly assess platform's effectiveness, adapt strategies and goals accordingly.

1. Platform adoption: Track the number of researchers, institutions, and indigenous communities using the platform.
2. Case studies: Document and analyze successful collaborations and research outcomes resulting from the platform integration.
3. Policy changes: Monitor policy shifts recognizing indigenous knowledge and protecting indigenous rights.
4. User satisfaction: Gather feedback from researchers and indigenous communities on platform effectiveness and cultural sensitivity.
5. Capacity building: Assess the increase in knowledge, skills, and collaboration between researchers and indigenous communities.

Our theory of change is centered around the idea that integrating AI and traditional indigenous knowledge systems can lead to more comprehensive, contextually relevant, and innovative research outcomes. By developing a platform that enables researchers to manage, label, and analyze data using both AI-driven analytics and indigenous wisdom, we expect to create a paradigm shift in research methodologies. This shift will encourage researchers to appreciate and value indigenous knowledge, leading to more inclusive and diverse research practices.

We involve indigenous communities in the development process to ensure their knowledge is represented accurately and ethically. As the platform gains adoption, we anticipate increased collaboration between researchers and indigenous communities, fostering mutual learning and respect. This collaboration will empower indigenous communities by preserving their knowledge and amplifying their voices in the research process.

Ultimately, by demonstrating the value of combining AI and indigenous knowledge systems, our solution will inspire others in the research ecosystem to adopt similar approaches, paving the way for broader positive impacts, policy changes, and a more inclusive research landscape.

1. AI-driven analytics: We leverage machine learning algorithms and natural language processing techniques to manage, label, and analyze research data, enabling researchers to uncover patterns, trends, and insights more efficiently.

2. Indigenous knowledge integration: Our platform incorporates traditional, ancestral, and natural knowledge systems, which are often underrepresented in research. We collaborate with indigenous communities to ensure their wisdom is embedded ethically and accurately within the platform.

3. Customizable interface: The platform offers a user-friendly interface that is adaptable to different research domains and indigenous cultures. This allows researchers to tailor the tools, methods, and knowledge systems to their specific needs and contexts.

We use the Indigenous-beliefs of: valuing all people and considering everyone. We believe in the sacredness of all living beings and value, respect, and welcome all. 

Our plan is to connect with others who can not only help reach this goal, but who also share the same values of diversity and consideration for all. Our main approach is openness and honestly, as well as working and hearing directly from the people we wish to serve.

For example, with our customers, we have worked directly from them and listened to their needs, their pain-points and wants. We have been open and receptive to all their thoughts. Within the tech industry, customers are typically not treated as people, but rather nameless consumers. We want to change that. We want to create a product that is accessible to all and that can work for everyone. 

By also incorporating these values directly into the technology, we create a new way of seeing data and AI, and question the ways in which it can be more free from bias. 

We will charge any institution that wants access to the software a monthly subscription fee depending usage. We provide them with access to a platform where they can manage, annotate, and analyze their data.

It is a SaaS model, and we are not reliant on donations. Our biggest expenditure (outside of labor) are the servers.

NSF iCorps Princeton grant, $3000. We went through a program with NSF that provided training in regards to customer outreach.