Promoting Equity in Medical AI

🌟 New Blueprint for Responsible AI in Healthcare! 🌟 Explore insights from Mass General Brigham's AI Governance Committee on implementing ethical AI in healthcare. This comprehensive study offers a detailed framework for integrating AI tools, ensuring fairness, safety, and effectiveness in patient care. Key Takeaways: 🔍 Core Principles for AI: The framework emphasizes nine key pillars—fairness, equity, privacy, safety, transparency, explainability, robustness, accountability, and patient benefit. 🤝 Multidisciplinary Collaboration: A team of experts from diverse fields established and refined these guidelines through literature review and hands-on case studies. 💡 Case Study: Ambient Documentation: Generative AI tools were piloted to streamline clinical note-taking, enhancing efficiency while addressing privacy and usability challenges. 📊 Continuous Monitoring: Dynamic evaluation metrics ensure tools adapt effectively to changing clinical practices and patient demographics. 🌍 Equity in Focus: The framework addresses bias by leveraging diverse training datasets and focusing on equitable outcomes for all patient demographics. This framework is a vital resource for healthcare institutions striving to responsibly adopt AI while prioritizing patient safety and ethical standards. #AIInHealthcare #ResponsibleAI #DigitalMedicine #GenerativeAI #EthicalAI #PatientSafety #HealthcareInnovation #AIEquity #HealthTech #FutureOfMedicine https://lnkd.in/gJqRVGc2

AI Is Misdiagnosing Millions—And No One's Talking Some patients are twice as likely to be misdiagnosed by AI. Why? The data that fuels it. In 2025, we’re seeing AI tools gain speed in healthcare. Faster triage. Faster decisions. Faster outcomes. But speed means nothing when it’s not fair. White patients are getting more accurate AI diagnoses. Black patients, Latino patients, Indigenous patients—less so. Why? Because systems are often trained on datasets that ignore demographic diversity. Because “representative” data is treated as an afterthought. Because fairness isn’t baked into the build—it’s patched in after launch. And for operations leaders pushing AI across the enterprise, this matters. Bias doesn’t just hurt ethics—it breaks performance. It leads to costly diagnostic errors. Regulatory exposure. Reputational risk. Fixing this starts with: • Training AI on inclusive, representative datasets • Stress-testing models across all populations • Demanding explainability from vendors—not just features • Making fairness a metric, not a footnote Healthcare transformation depends on trust. Without equity, there is no trust. Without trust, AI fails. If you're scaling AI in regulated environments, how are you building fairness into your rollout plans? CellStrat #CellBot #HealthcareAI

Insights from our recent publication in Nature Medicine: Sociodemographic Biases in Medical Decision Making by Large Language Models As AI continues to shape clinical decision-making, our study reveals an urgent challenge: LLMs often recommend different care—sometimes more invasive, sometimes less—for patients based on race, gender identity, income, or housing status, even when clinical details are identical. We analyzed 1.7 million AI-generated outputs from 9 leading models. Key findings: 1. LGBTQIA+ patients were 6–7x more likely to be flagged for mental health assessments than clinically warranted 2. High-income patients were steered toward advanced diagnostics; low-income patients often weren’t 3. Black, unhoused, and transgender patients were disproportionately triaged as urgent—even without clinical justification Read the study here 👉 https://lnkd.in/ejnGpGCt As we focus on embedding AI responsibly in healthcare, this work underscores a fundamental truth: we can't separate innovation from equity. It must be baked in from the start. Proud to work alongside brilliant colleagues advancing this vital research: Girish Nadkarni, Alexander Charney, Eyal Klang, Ben Glicksberg, Mahmud Omar, Shelly Soffer, MD, Benjamin Kummer, MD, Carol Horowitz, MD, MPH,Donald Apakama,Reem Agbareia, Nicola Luigi Bragazzi #Equity #HealthcareAI

Promoting transparency and #trustworthy AI exploration is paramount for a healthier, more equitable future. President Biden's recent call for responsible AI in healthcare has garnered voluntary pledges from industry leaders, including Allina Health and CVS Health, rallying around the 'FAVES' principles. These principles focus on ensuring fair, appropriate, valid, effective, and safe AI applications, prioritizing patient safety, equity, and affordability while driving innovation. #Healthcare systems will eventually have to address the concerns about providing "de-identified" data to 3rd parties or using it for AI training—a risk in itself. We will see how much of this plays out in the courts for other industries. The most risk-averse option for a sensitive industry, such as healthcare, would be to get informed #consent. "Absent proper oversight, diagnoses by AI can be biased by gender or race, especially when AI is not trained on data representing the population it is being treated. Additionally, AI's ability to collect large volumes of data—and infer new information from disparate datapoints—could create privacy risks for patients. All these risks are vital to address." This initiative is crucial in recognizing AI's transformative potential in healthcare. It also highlights the need for rigorous oversight to address risks such as bias, errors, and #privacy breaches. These principles are core to our mission at Destined AI as we empower better healthcare outcomes with less bias. #AIinHealthcare #HealthTech #TrustworthyAI https://lnkd.in/ePg_bZJm

🧠 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

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