Health Equity Strategies in AI

The one-size-fits-all approach does not address ever present inequalities. Bring together more stakeholders, define fairness and equity, and develop models that achieve specific goals - specific to those demographics that need new solutions. The general deployment of #AI without the consideration of equity at every stage of development will continue to perpetuate the inequalities we originally aimed to address. "...aspiring to achieve health equity requires considering that individuals with ”larger barriers to improving their health require more and/or different, rather than equal, effort to experience this fair opportunity.” Equity does not equate to the fairness of AI predictions and diagnoses, which aspires to have equal performance across all populations, with no regard for these populations’ differential needs and processes." #healthcare #healthcareonlinkedin #artificialintelligence #healthequity

‼️ 🌟 Artificial Intelligence & Cancer Health Equity 🌟 ⚖️ My co-authors (Kingsley I. Ndoh, Darlington Akogo, Hermano Alexandre Lima Rocha, Sergio Juacaba) and I are pleased to share our latest publication in *Current Oncology Reports: Artificial Intelligence and Cancer Health Equity. This paper explores the potential of AI technologies in cancer diagnosis, treatment, and patient care—while critically examining the risks of perpetuating disparities if we don’t center equity in its design, development and deployment. The promise of AI and advanced technologies in oncology is undeniable. The stark reality is that biases in training data, unequal access to technology and digital health tools, and systemic barriers may widen cancer disparities rather than close them. That’s also why it’s crucial to #spotlight organizations committed to building inclusive technologies, digital health, consumer health tools and AI solutions to address cancer care ethically and equitably. Our paper highlights leaders in this space, including: CancerIQ – Empowering healthcare providers with AI-driven risk assessment tools for early cancer detection and prevention Rede ICC Saúde / Ceará Cancer Institute – Integrating AI into oncology care to improve access in Brazil COTA, Inc. – Using real-world data to uncover and address cancer care disparities Flatiron Health – Harnessing real-world data and AI to drive precision oncology and ensure that insights from cancer research benefit all populations Freenome – Advancing early cancer detection through AI-powered multi-omics Hologic, Inc. – Advancing women's health with AI-powered breast cancer screening solutions designed for equitable access Hurone AI – Bringing AI-driven oncology solutions to underserved communities globally minoHealth AI Labs – Leveraging AI to improve cancer diagnostics and clinical decision support in Africa Patient Discovery – Using AI and patient-reported data to personalize care and reduce barriers for diverse cancer patients Vectorgram Health – Enhancing cancer diagnostics and care in sub-Saharan Africa These are just a few of the #trailblazers ensuring that advanced technologies in cancer care does not leave anyone behind. THANK YOU! Our call to action? Technology and digital health tools must be built for everyone. That means inclusive and diverse datasets, ethical frameworks, and policies that prioritize equity at every stage of AI development. Read the full paper here: #AI #CancerCare #DigitalHealth #HealthEquity #ArtificialIntelligence #PrecisionMedicine #Oncology #MachineLearning #DiversityInAI

🧠 With proposed Medicaid cuts and work requirements on the horizon…could AI help our health systems continue to serve Medicaid patients effectively—without deepening disparities? The House Republicans' latest budget proposal includes over $880 billion in federal healthcare cuts, with $715 billion coming from devastating cuts to Medicaid. It introduces stricter eligibility checks, reduces federal Medicaid contributions, and enforces work requirements for many recipients. These changes could result in 8.6 million Americans losing coverage, and as reported by CAP, will result in substantial loss of life. https://shorturl.at/8MZgs For health systems already stretched thin, what can we do to prevent this devastating situation? Might AI tools help to extend our capacity while not widening inequities? Three ideas that might help… ① Streamlined documentation: With more coverage redetermination, eligibility checks, and work requirements comes a ton of new paperwork. AI tools could scour and prove eligibility preventing hundreds of thousands from losing Medicaid coverage. ② Social risk screening & connection: AI tools could automate social risk screening efforts, improve the mapping of available social services, and connect patients who need those services more continuously via voice agents. Not to mention the availability of communication in countless languages via AI translation services. ③ Augmented decision-making at the point of care: This one might be further off, but I wonder whether we might soon see LLM-powered assistants that help primary care clinicians personalize care plans, predict rising social and clinical risk, and facilitate more person-centered care. Even with these improvements, I still worry that many will lose coverage and access to critical care services. Helping our providers and health system partners serve our patients who need Medicaid is now more important than ever. 🗣️ What creative ideas do you have for how we can leverage investments in AI (and other areas) to benefit those who will need care the most in the future? #HealthcareAI #MedicaidInnovation #HealthEquity #PatientSafety #DigitalHealth #AIinHealthcare