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Artificial Intelligence (AI) is transforming healthcare by enhancing health literacy and making medical information more accessible. AI-powered tools can simplify complex medical terminology by helping patients better understand their diagnoses and treatment plans by overcoming language barriers. This personalized approach improves patient adherence to medical advice by ultimately leading to improved health outcomes (3). Additionally, AI helps to bridge healthcare gaps in rural and underserved areas by providing essential medical information and support where healthcare professionals are scarce (1). AI’s ability to process vast amounts of data quickly might also contribute to improved diagnostic accuracy. In a breast cancer screening study, AI-powered tools demonstrated a high accuracy, outperforming radiologists in detecting breast cancer from mammograms alone. AI-powered tools reduced false positives by 5.7% and false negatives by 9.4% in U.S. datasets (5). By streamlining communication and care delivery, AI is proving to be an invaluable tool in modern healthcare. While AI offers significant advancements in healthcare, it also presents notable challenges, particularly regarding bias and discrimination. Racial bias present in various algorithms underestimates the health risks of Black patients despite their higher prevalence of active chronic conditions compared to White patients’ due to biases in training data (6). AI algorithms rely on large datasets for learning but if these datasets are not diverse, the technology can exhibit bias towards vulnerable populations and reinforce existing disparities in healthcare outcomes. Research indicates that AI tools may be less accurate in diagnosing conditions among marginalized groups due to their underrepresentation in training data (2). Additionally, AI-generated health recommendations can sometimes misinterpret symptoms or provide misleading information especially in the absence of human oversight or if physicians lack proper training in the prompts to provide AI-powered tools (7). Beyond accuracy and bias, ethical concerns surrounding data privacy and patient consent remain critical as AI-driven systems rely on vast amounts of personal health data to function effectively (8). AI has a complex ‘black box’ nature lacking diagnostic transparency and regulatory oversight (9) and may affect the patient-provider relationship at the cost of human connection and empathy (10). Addressing these challenges is vital to ensuring that AI serves all populations equitably and accurately. To maximize AI’s benefits while mitigating its risks, healthcare professionals and policymakers must establish clear guidelines that promote ethical and responsible AI use (8). Ensuring AI’s responsible integration into healthcare requires adherence to global standards and continuous oversight. The FUTURE-AI Consortium, a collaboration of 117 experts from 50 countries, has developed international guidelines for trustworthy AI in medicine (4). Their framework is built around six key principles: fairness, universality, traceability, usability, robustness, and explainability. These principles guide the entire AI lifecycle ensuring AI systems align with real-world healthcare needs and ethical considerations. The framework includes 30 detailed recommendations that emphasize collaboration among policymakers, healthcare providers, and AI developers. FUTURE-AI is designed as a dynamic framework that evolves alongside technological advancements and stakeholder feedback to maintain safety, transparency, and effectiveness in healthcare AI applications (4). Continuous risk assessment and mitigation are essential for addressing biases, data variations, and emerging challenges. As AI research continues to advance, it is crucial for policymakers and healthcare professionals to stay informed and prepared to respond swiftly to new developments and concerns to ensure responsible use (2).
Decoded: Navigating Health in the Digital Age
Tuesday, April 1, 2025
Artificial Intelligence (AI) is transforming healthcare by enhancing health literacy and making medical information more accessible. AI-powered tools can simplify complex medical terminology by helping patients better understand their diagnoses and treatment plans by overcoming language barriers. This personalized approach improves patient adherence to medical advice by ultimately leading to improved health outcomes (3). Additionally, AI helps to bridge healthcare gaps in rural and underserved areas by providing essential medical information and support where healthcare professionals are scarce (1). AI’s ability to process vast amounts of data quickly might also contribute to improved diagnostic accuracy. In a breast cancer screening study, AI-powered tools demonstrated a high accuracy, outperforming radiologists in detecting breast cancer from mammograms alone. AI-powered tools reduced false positives by 5.7% and false negatives by 9.4% in U.S. datasets (5). By streamlining communication and care delivery, AI is proving to be an invaluable tool in modern healthcare. While AI offers significant advancements in healthcare, it also presents notable challenges, particularly regarding bias and discrimination. Racial bias present in various algorithms underestimates the health risks of Black patients despite their higher prevalence of active chronic conditions compared to White patients’ due to biases in training data (6). AI algorithms rely on large datasets for learning but if these datasets are not diverse, the technology can exhibit bias towards vulnerable populations and reinforce existing disparities in healthcare outcomes. Research indicates that AI tools may be less accurate in diagnosing conditions among marginalized groups due to their underrepresentation in training data (2). Additionally, AI-generated health recommendations can sometimes misinterpret symptoms or provide misleading information especially in the absence of human oversight or if physicians lack proper training in the prompts to provide AI-powered tools (7). Beyond accuracy and bias, ethical concerns surrounding data privacy and patient consent remain critical as AI-driven systems rely on vast amounts of personal health data to function effectively (8). AI has a complex ‘black box’ nature lacking diagnostic transparency and regulatory oversight (9) and may affect the patient-provider relationship at the cost of human connection and empathy (10). Addressing these challenges is vital to ensuring that AI serves all populations equitably and accurately. To maximize AI’s benefits while mitigating its risks, healthcare professionals and policymakers must establish clear guidelines that promote ethical and responsible AI use (8). Ensuring AI’s responsible integration into healthcare requires adherence to global standards and continuous oversight. The FUTURE-AI Consortium, a collaboration of 117 experts from 50 countries, has developed international guidelines for trustworthy AI in medicine (4). Their framework is built around six key principles: fairness, universality, traceability, usability, robustness, and explainability. These principles guide the entire AI lifecycle ensuring AI systems align with real-world healthcare needs and ethical considerations. The framework includes 30 detailed recommendations that emphasize collaboration among policymakers, healthcare providers, and AI developers. FUTURE-AI is designed as a dynamic framework that evolves alongside technological advancements and stakeholder feedback to maintain safety, transparency, and effectiveness in healthcare AI applications (4). Continuous risk assessment and mitigation are essential for addressing biases, data variations, and emerging challenges. As AI research continues to advance, it is crucial for policymakers and healthcare professionals to stay informed and prepared to respond swiftly to new developments and concerns to ensure responsible use (2).
Beyond the Diagnosis: Exploring Mental Health & Health Literacy
Tuesday, April 1, 2025
Artificial Intelligence (AI) is transforming healthcare by enhancing health literacy and making medical information more accessible. AI-powered tools can simplify complex medical terminology by helping patients better understand their diagnoses and treatment plans by overcoming language barriers. This personalized approach improves patient adherence to medical advice by ultimately leading to improved health outcomes (3). Additionally, AI helps to bridge healthcare gaps in rural and underserved areas by providing essential medical information and support where healthcare professionals are scarce (1). AI’s ability to process vast amounts of data quickly might also contribute to improved diagnostic accuracy. In a breast cancer screening study, AI-powered tools demonstrated a high accuracy, outperforming radiologists in detecting breast cancer from mammograms alone. AI-powered tools reduced false positives by 5.7% and false negatives by 9.4% in U.S. datasets (5). By streamlining communication and care delivery, AI is proving to be an invaluable tool in modern healthcare. While AI offers significant advancements in healthcare, it also presents notable challenges, particularly regarding bias and discrimination. Racial bias present in various algorithms underestimates the health risks of Black patients despite their higher prevalence of active chronic conditions compared to White patients’ due to biases in training data (6). AI algorithms rely on large datasets for learning but if these datasets are not diverse, the technology can exhibit bias towards vulnerable populations and reinforce existing disparities in healthcare outcomes. Research indicates that AI tools may be less accurate in diagnosing conditions among marginalized groups due to their underrepresentation in training data (2). Additionally, AI-generated health recommendations can sometimes misinterpret symptoms or provide misleading information especially in the absence of human oversight or if physicians lack proper training in the prompts to provide AI-powered tools (7). Beyond accuracy and bias, ethical concerns surrounding data privacy and patient consent remain critical as AI-driven systems rely on vast amounts of personal health data to function effectively (8). AI has a complex ‘black box’ nature lacking diagnostic transparency and regulatory oversight (9) and may affect the patient-provider relationship at the cost of human connection and empathy (10). Addressing these challenges is vital to ensuring that AI serves all populations equitably and accurately. To maximize AI’s benefits while mitigating its risks, healthcare professionals and policymakers must establish clear guidelines that promote ethical and responsible AI use (8). Ensuring AI’s responsible integration into healthcare requires adherence to global standards and continuous oversight. The FUTURE-AI Consortium, a collaboration of 117 experts from 50 countries, has developed international guidelines for trustworthy AI in medicine (4). Their framework is built around six key principles: fairness, universality, traceability, usability, robustness, and explainability. These principles guide the entire AI lifecycle ensuring AI systems align with real-world healthcare needs and ethical considerations. The framework includes 30 detailed recommendations that emphasize collaboration among policymakers, healthcare providers, and AI developers. FUTURE-AI is designed as a dynamic framework that evolves alongside technological advancements and stakeholder feedback to maintain safety, transparency, and effectiveness in healthcare AI applications (4). Continuous risk assessment and mitigation are essential for addressing biases, data variations, and emerging challenges. As AI research continues to advance, it is crucial for policymakers and healthcare professionals to stay informed and prepared to respond swiftly to new developments and concerns to ensure responsible use (2).
Bridging the Gap: The Role of Health Literacy in Reducing Healthcare Inequalities
Tuesday, April 1, 2025
Artificial Intelligence (AI) is transforming healthcare by enhancing health literacy and making medical information more accessible. AI-powered tools can simplify complex medical terminology by helping patients better understand their diagnoses and treatment plans by overcoming language barriers. This personalized approach improves patient adherence to medical advice by ultimately leading to improved health outcomes (3). Additionally, AI helps to bridge healthcare gaps in rural and underserved areas by providing essential medical information and support where healthcare professionals are scarce (1). AI’s ability to process vast amounts of data quickly might also contribute to improved diagnostic accuracy. In a breast cancer screening study, AI-powered tools demonstrated a high accuracy, outperforming radiologists in detecting breast cancer from mammograms alone. AI-powered tools reduced false positives by 5.7% and false negatives by 9.4% in U.S. datasets (5). By streamlining communication and care delivery, AI is proving to be an invaluable tool in modern healthcare. While AI offers significant advancements in healthcare, it also presents notable challenges, particularly regarding bias and discrimination. Racial bias present in various algorithms underestimates the health risks of Black patients despite their higher prevalence of active chronic conditions compared to White patients’ due to biases in training data (6). AI algorithms rely on large datasets for learning but if these datasets are not diverse, the technology can exhibit bias towards vulnerable populations and reinforce existing disparities in healthcare outcomes. Research indicates that AI tools may be less accurate in diagnosing conditions among marginalized groups due to their underrepresentation in training data (2). Additionally, AI-generated health recommendations can sometimes misinterpret symptoms or provide misleading information especially in the absence of human oversight or if physicians lack proper training in the prompts to provide AI-powered tools (7). Beyond accuracy and bias, ethical concerns surrounding data privacy and patient consent remain critical as AI-driven systems rely on vast amounts of personal health data to function effectively (8). AI has a complex ‘black box’ nature lacking diagnostic transparency and regulatory oversight (9) and may affect the patient-provider relationship at the cost of human connection and empathy (10). Addressing these challenges is vital to ensuring that AI serves all populations equitably and accurately. To maximize AI’s benefits while mitigating its risks, healthcare professionals and policymakers must establish clear guidelines that promote ethical and responsible AI use (8). Ensuring AI’s responsible integration into healthcare requires adherence to global standards and continuous oversight. The FUTURE-AI Consortium, a collaboration of 117 experts from 50 countries, has developed international guidelines for trustworthy AI in medicine (4). Their framework is built around six key principles: fairness, universality, traceability, usability, robustness, and explainability. These principles guide the entire AI lifecycle ensuring AI systems align with real-world healthcare needs and ethical considerations. The framework includes 30 detailed recommendations that emphasize collaboration among policymakers, healthcare providers, and AI developers. FUTURE-AI is designed as a dynamic framework that evolves alongside technological advancements and stakeholder feedback to maintain safety, transparency, and effectiveness in healthcare AI applications (4). Continuous risk assessment and mitigation are essential for addressing biases, data variations, and emerging challenges. As AI research continues to advance, it is crucial for policymakers and healthcare professionals to stay informed and prepared to respond swiftly to new developments and concerns to ensure responsible use (2).
The Role of AI in Health Literacy: Benefits, Concerns, and Call to Action
Tuesday, April 1, 2025
UIC hosted The Menopause Imperative: Advancing Care, Research, and Policy
Thursday, March 13, 2025
On March 13, UIC hosted The Menopause Imperative: Advancing Care, Research, and Policy, a dynamic event convening international, national, and local leaders across sectors to highlight the urgent need for menopause awareness, research, and quality ca
The Acceptability and Feasibility of Self-Collected HPV Testing for Cervical Cancer Screening Among Black and Latinx Women in Chicago: Perspectives from the Community
Friday, March 7, 2025
We are thrilled to announce the publication of this manuscript in Women's Health Reports!
CRWG Senior Research Specialists Presented at UIC College of Medicine Research Forum
Friday, January 24, 2025
CRWG Senior Research Specialists Emilie Glass-Riveros, Katherine Craemer, and Lauren Sayah presented at the University of Illinois College of Medicine (COM) Research Forum. CRWG Director Dr. Stacie Geller also attended the Forum.
Beatriz Penalver-Bernave received an R21 Trailblazer Award
Friday, January 24, 2025
Beatriz Penalver-Bernave received an R21 Trailblazer Award for New and Early Stage Investigators grant from the National Institute of Aging (NIA/NIH) entitled “Utilizing spatially-informed cell-specific multicellular genome-scale metabolic models to
CRWG Alice J. Dan Dissertation Award Winner, Alexandra Paget-Blanc, was awarded a grant from NIH and a poster award from Menopause Society
Friday, January 24, 2025
CRWG Alice J. Dan Dissertation Award Winner, Alexandra Paget-Blanc, was awarded a grant from NIH and a poster award from Menopause Society
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College of Medicine News from UIC Today Heading link

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Professor named 2024-25 Distinguished Researcher in Clinical Sciences
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Professor named 2024-25 Distinguished Researcher in Basic Life Sciences
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This year, 274 medical students from the three College of Medicine campuses were matched with programs nationwide
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Blood test could help clinicians measure depression
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UIC study expands previous work on promising signal of depression symptoms