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I think that AI and ML could improve patient care by providing a way for individuals living in rural areas who don't have close proximity
access to healthcare facilities could use the virtual exam to assist the healthcare provider in making a diagnosis when the patient is not receiving care in person. The risk would be that the healthcare provider may rely more on the model than their own clinical judgement which would normally be influenced by nonverbal cues that would be more difficult to pick up in a virtual setting.

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Diverse datasets can lead to more balanced healthcare systems. They can lead to faster screenings for diseases, optimized treatment strategies, and increase access to medical care. In addition, if the diverse datasets are distributed to most hospitals and treatment centers this can help clinicians treat patients that aren't their typical patient demographic or needing different diagnosis tools or treatments.

Nettie Brown

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Equitable data would help researchers and clinicians understand larger populations. We've seen through research that models like BMI are not applicable to everyone. So equitable data can help create better standards and models for equitable health research initiatives.

Nettie Brown

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Building a Bridge between AI/ML & Health Care

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Building a Bridge between AI/ML & Health Care

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It is hard to choose just one idea and benefit to the integration of AI/ML into healthcare. Some of the greatest benefits include improving fairness while reducing bias, enhancing generalizability, addressing SDoH, and ethical and regulatory compliance. By prioritizing diverse datasets and inclusive AI design, we can build a more equitable, effective, and unbiased healthcare system that benefits all patients, not just those who are best represented in traditional datasets.

Payton Mendygral

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Potential Applications: One major way AI can improve patient care is through early disease detection and diagnosis. For example, AI-powered imaging tools can analyze radiology scans (e.g., MRIs, CTs) to detect cancers or neurological disorders faster and more accurately than human radiologists, leading to earlier interventions and better patient outcomes.

Potential Risks: A key risk is bias in AI models, where algorithms trained on non-representative data can lead to inaccurate or unfair predictions, particularly for underrepresented populations. This could result in misdiagnoses or disparities in treatment recommendations. Additionally, over-reliance on AI could reduce clinicians’ critical thinking skills, and errors in AI decision-making could have serious patient safety consequences.

Payton Mendygral

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How can diverse datasets and inclusive AI design help create a more balanced healthcare system?

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How can diverse datasets and inclusive AI design help create a more balanced healthcare system?

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Inclusive AI and diverse datasets help ensure healthcare solutions address the needs of all populations. By incorporating data from underrepresented groups, AI can provide personalized treatments and reduce disparities, leading to more equitable and effective care for everyone.

Sadaf Ghaderzadeh

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Consider the potential applications and impacts of AI and ML in Healthcare. What’s one way AI could improve patient care? What risks might come with it?

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Consider the potential applications and impacts of AI and ML in Healthcare. What’s one way AI could improve patient care? What risks might come with it?

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AI can improve patient care by quickly and accurately diagnosing conditions, especially through medical imaging, which leads to faster treatment. However, risks include over-reliance on AI and potential algorithmic bias, especially if training data isn't diverse. Ensuring diverse data and proper oversight can help minimize these issues.

Sadaf Ghaderzadeh

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Consider this example and the image demonstrating the difference between equality and equity. How might equitable data access influence research outcomes? How could a similar approach help other health research initiatives become more inclusive? 

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Consider this example and the image demonstrating the difference between equality and equity. How might equitable data access influence research outcomes? How could a similar approach help other health research initiatives become more inclusive? 

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Equitable data access helps ensure that all populations, including underserved ones, are represented in research. In the case of N3C during COVID-19, it allowed smaller institutions to contribute, leading to more inclusive findings about how the virus impacted different groups. For other health research, giving equal access to data and resources can make studies more representative and help identify health disparities, ultimately improving outcomes for everyone, especially those who are often overlooked.

Sadaf Ghaderzadeh

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What’s the biggest challenge in using AI/ML in healthcare today, and how would you address it?

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What’s the biggest challenge in using AI/ML in healthcare today, and how would you address it?

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Challenge: One major hurdle in healthcare AI/ML is integrating data from various departments and systems that use different formats and standards. This often leads to confusion or errors when the models try to process the data, especially in cases like patient records or diagnostic information.

Solution: To tackle this, we could introduce a "data translator" role within the hospital, essentially someone who bridges the gap between IT, clinicians, and data scientists. They'd ensure that data is appropriately formatted and standardized before feeding it into the AI systems, making it easier for the models to analyze and produce accurate results. Having a clear, common data structure across departments would improve not only AI model performance but also trust in the system from clinicians who rely on it.

Sadaf Ghaderzadeh

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In an ideal scenario, AI can provide quick and accurate care. This technology can help aid significantly more people than an individual doctor or practitioner. However, that's an ideal scenario and that's assuming the system is accurate with diagnoses and recommendations. If they are not, they can cause more harm than good.

Armisha Roberts

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I'm unfamiliar with the extent of AI/ML use specifically in healthcare, so I am aware of my broad generalization. However, there is a strong belief that I've witnessed in other sectors to believe the output of various ML models without heavy scrutiny. That's dangerous and can perpetuate numerous problems witnessed in society because these data do not accurately represent communities, essentially, trash in/trash out. There needs to be thoughtful action and effort in the data that is collected and the diversity of the data.

Armisha Roberts

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Diverse datasets and inclusive AI design help reduce biases, ensuring accurate diagnoses and treatments for all populations. This fosters a more equitable healthcare system by addressing disparities in medical research and care.

Ryan K. Perez

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AI can improve patient care by enabling early disease detection through predictive analytics, leading to timely interventions. However, risks include biased algorithms and misdiagnoses, emphasizing the need for rigorous validation and oversight.

Ryan K. Perez

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Equitable data access enables diverse researchers to study health disparities, leading to more representative and impactful findings. Expanding training and resources in other health initiatives can ensure inclusive, community-driven scientific advancements.

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AI/ML in healthcare can inherit biases, leading to unequal treatment. To address this, we must use diverse training data, conduct regular bias audits, and implement continuous model monitoring for fairness.

Ryan K. Perez

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To bridge the gap, strict guidelines and rigorous validation processes should be in place to continuously monitor, audit, and verify AI-driven decisions, ensuring they do not reinforce biases or worsen health disparities. Responsible AI development and deployment in healthcare must prioritize transparency, accountability, and ethical oversight to build trust and reliability while maximizing its benefits.

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AI/ML tools can be utilized in personalized and autonomous decision support workflows, enabling early detection of disease progression or adverse health outcomes, such as sepsis, ER visits, or hospitalizations. The tools allow clinicians to intervene earlier. However, if the AI/ML tools/algorithms are biased or fail to fairly represent all populations equitably, they could exacerbate healthcare disparities, leading to unequal access to accurate diagnoses and treatments, potentially even worsening patient outcomes for marginalized communities. Ensuring equitable data representation and mitigating bias is important for developing AI/ML tools to benefit all patients fairly.

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Equitable data access enables a more diverse group of researchers to study topics traditionally underrepresented in research, particularly when data is integrated and reflects diverse populations. This effort fosters more inclusive, comprehensive, and representative findings, helping to address health disparities and improve outcomes for underserved communities.

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AI/ML can worsen health disparities if trained on biased data. It can underprioritize minority or certain populations in clinical decisions, lead to inequitable care, and exacerbate healthcare inequities.

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Activity #4: Bridging the Gap with AI/ML & Health Equity

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Activity #4: Bridging the Gap with AI/ML & Health Equity

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A more diverse system is more inclusive and has a greater ability to cover variances between patients that differ even among similar body types. By building in that range you give the system more knowledge to work with.

Loni Taylor

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Activity #3: AI/ML’s Superpowers in Healthcare

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Activity #3: AI/ML’s Superpowers in Healthcare

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How can we improve care with AI? We include a wider range of people. That way you can highlight anomalies and know where to start looking when something is bothering a patient. You may not can cure them, but you can stop giving them treatment plans that are just a shot in the dark based on assumptions. Right now AI is so limited because it mirrors, models, predicts and/or analyzes what's available. When you are missing a significant range of data samples from one group in comparison to another that AI is then faulty or at the very least ill-fitting.

Loni Taylor

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Activity #2: Equality vs. Equity: Not Just Buzzwords

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Activity #2: Equality vs. Equity: Not Just Buzzwords

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One of the things I wish was considered at the time was more than just the impact of the virus. Underrepresented groups would have benefitted from the collaborative atmosphere when it came to the effectiveness of the vaccine as well. While the access to it was probably delivered with equity, the results on the overall health and future health outcomes did not receive that same consideration.

Loni Taylor

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Activity #1: Overcoming AI/ML Challenges in Healthcare

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Activity #1: Overcoming AI/ML Challenges in Healthcare

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Data representation covering underrepresented groups is a problem. A solution to overcoming this problem is to increase trust centered around malicious intent by fully disclosing the intent and methodology around collection.

Loni Taylor

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Overcoming AI/ML Challenges in Healthcare

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Overcoming AI/ML Challenges in Healthcare

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A key challenge in healthcare AI is biased data impacting diagnoses. To prioritize equity, we must set our compass on representation, assess and address potential bias, and keep humans in the loop. Murad Moqbel

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equitable data access ensures diverse representation in research, leading to more accurate and inclusive health outcomes. Applying this approach broadly can reduce disparities and improve healthcare solutions for underrepresented communities. Murad Moqbel

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AI can improve patient care by enabling early disease detection through predictive analytics. However, risks include biased algorithms that may lead to misdiagnoses, emphasizing the need for continuous monitoring and human oversight. Murad Moqbel

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Bridging the Gap with AI/ML & Health Equity

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Bridging the Gap with AI/ML & Health Equity

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Diverse datasets and inclusive AI design ensure accurate, unbiased diagnoses for all populations, reducing health disparities and improving patient outcomes, especially in underrepresented communities. Equity-focused AI leads to fairer healthcare solutions Murad

# data[].attributes.headline
Bridging the Gap with AI/ML & Health Equity

# data[].attributes.subject
Bridging the Gap with AI/ML & Health Equity

# data[].attributes.body
Diverse datasets and inclusive AI design ensure accurate, unbiased diagnoses for all populations, reducing health disparities and improving patient outcomes, especially in underrepresented communities. Equity-focused AI leads to fairer healthcare solutions Murad Moqbel

# data[].attributes.body
AI can improve patient care by enabling early disease detection through predictive analytics. However, risks include biased algorithms that may lead to misdiagnoses, emphasizing the need for continuous monitoring and human oversight.

# data[].attributes.body
Equitable data access ensures diverse representation in research, leading to more accurate and inclusive health outcomes. Applying this approach broadly can reduce disparities and improve healthcare solutions for underrepresented communities.

# data[].attributes.headline
Overcoming AI/ML Challenges in Healthcare

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Overcoming AI/ML Challenges in Healthcare

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A key challenge in healthcare AI is biased data impacting diagnoses. To prioritize equity, we must set our compass on representation, assess and address potential bias, and keep humans in the loop.

# data[].attributes.body
One challenge in using AI/ML is bias in AI models, which can reinforce health disparities. I would address this issue by utilizing fairness audits on the AI models, ensure diverse and representative samples are used to train the AI model, and all decision-making is equitable for all patient populations.

Scherrayn Phillip-Garcia

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AI/ML resources are evolving and most healthcare organizations experience a significant "knowledge to practice gap" in translating latest and most effective resources available. I argue that robust policies and innovative practices should be adopted promoting resilient adoption of evidence-based AI/ML resources in healthcare preventing these gaps and facilitating high-quality care.

M. Mahbub Hossain

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AI/ML magic bullet: Separating signal from noise

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AI/ML magic bullet: Separating signal from noise

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AI/ML can help seeing the bigger and clearer version of truth in a given context leveraging big data perspectives. That may help identifying signals for action and mitigate noises in data resources.

M. Mahbub Hossain

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AI/ML researchers and practitioners need to reflect on the broader social and health crises we have in the US and globally, and recognize the significance of equity-focused approaches to address those problems. While equality is a common goal and easy to understand concept, a deeper perspective may inform the roots of inequalities that originate from socio-economic inequities in a population. These challenges need to be recognized and reflected in AI/ML use-related policies and practices to advance health equity across populations.

M. Mahbub Hossain

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One of the critical challenges is findings the specific AI/ML resources that would best serve the needs of healthcare providers in clinical settings. There are many models and data sources but finding the more useful and scientifically accurate ones remain a continued challenge.

M. Mahbub Hossain

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AI models trained on diverse and representative data can reduce racial, gender, and socioeconomic disparities in healthcare, leading to personalized and fairer treatment and improved patient trust in AI-assisted decisions. -Janette Vazquez

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Overcoming AI/ML Challenges in Healthcare

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Overcoming AI/ML Challenges in Healthcare

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One of the challenges with AI/ML is generalizability of findings. A way to address this is using multi-site and diverse datasets, as well as external validation techniques to ensure robustness. -Janette Vazquez

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Equitable data access might influence research outcomes because it allows access to broader participation and the potential for unique perspectives that might have been unheard. A similar approach of providing training and support can lead to a more inclusive collaboration and diverse perspectives. Gloria Boone

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AI/ML can improve patient care through predictions that might lead to early disease detection. Risks include misdiagnosis associated with AI/ML model bias. Gloria Boone

# data[].attributes.body
Equitable data access and inclusive AI design can reshape healthcare by addressing disparities and improving outcomes for marginalized groups. Diverse datasets ensure AI models are representative, reducing biases that perpetuate inequities, such as underdiagnosis in minority populations. Inclusive design, involving diverse stakeholders, ensures AI tools meet varied community needs and cultural contexts. This approach fosters trust, enhances diagnostic accuracy, and enables personalized care for underserved groups.

Olabisi Ojo

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AI can improve patient care by enhancing diagnostic accuracy through medical imaging analysis. However, risks include algorithmic bias, which can perpetuate health disparities, and overreliance on AI, potentially diminishing clinicians' critical thinking. Addressing these risks requires diverse datasets for training, transparent validation processes, and ongoing clinician education to maintain balanced decision-making.

Olabisi Ojo

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Equitable data access, as demonstrated by N3C's efforts, ensures that researchers from underfunded or underserved institutions can contribute to and benefit from large-scale studies, fostering diverse perspectives and inclusive findings. By addressing disparities in resources, such as through shared computational tools and training, research outcomes better reflect the needs of marginalized populations, as shown in the equity image. Applying this approach to other health initiatives—like cancer or chronic disease research—could uncover disparities, improve interventions for vulnerable groups, and advance health equity overall.

-Olabisi Ojo

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A major challenge is algorithmic bias. This can be addressed via co-designed workflows with clinicians/staff training on data equity audits and real-time feedback loops to ensure inclusive AI validation.

Olabisi Ojo

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Activity #4: Bridging the Gap with AI/ML & Health Equity

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Activity #4: Bridging the Gap with AI/ML & Health Equity

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Diverse datasets and inclusive AI design are vital for creating a more balanced healthcare system. By incorporating data from various populations, AI models can better reflect the true diversity of patients. This helps in identifying unique health trends and disparities among different groups, leading to more accurate diagnoses and effective treatments.

Inclusive AI design ensures that the models are trained to recognize and address biases, thus providing fair and equitable care for all. When AI systems are built with inclusivity in mind, they can help reduce health disparities and ensure that every individual receives the best possible care, regardless of their background. This approach fosters trust in technology and promotes better health outcomes across the board.

Farzana Hussain

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Activity #3: AI/ML’s Superpowers in Healthcare

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Activity #3: AI/ML’s Superpowers in Healthcare

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One way AI can improve patient care is through personalized treatment plans. By analyzing a patient’s medical history, genetic information, and lifestyle data, AI can recommend tailored treatments that optimize outcomes.

However, this comes with risks such as data privacy concerns, potential biases in AI algorithms, and over-reliance on technology. Ensuring robust data protection, transparency in AI decision-making, and integrating human oversight can help mitigate these risks.

_Farzana Hussain.

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Activity #2: Equality vs. Equity: Not Just Buzzwords

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Activity #2: Equality vs. Equity: Not Just Buzzwords

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Equitable data access allows a diverse range of researchers to contribute, which enhances the richness and inclusivity of research outcomes. By including varied perspectives and local knowledge, we can identify and address health disparities more effectively.

A similar approach can democratize access to valuable data in other health research initiatives. Training, shared computational resources, and partnerships with smaller institutions can empower underfunded organizations to participate. This could lead to more comprehensive research findings that better represent diverse populations, ultimately leading to improved health outcomes for all.

-Farzana Hussain

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Activity #1: Overcoming AI/ML Challenges in Healthcare

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Activity #1: Overcoming AI/ML Challenges in Healthcare

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The biggest challenge is ensuring data privacy. To address it, we can use advanced encryption and anonymization techniques to safeguard patient data while enabling effective AI/ML analysis.

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AI can help avoid delays in treatment by early disease detection but needs to be handled with care

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AI can help avoid delays in treatment by early disease detection but needs to be handled with care

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AI solutions possess great power to improve timely detection of a variety of health conditions, thereby avoiding treatment delays; however, it is important to ensure that the models are trained with unbiased data representing a wide range of demographics to ensure health equity. - Veronica Wallaengen

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I believe one of the biggest challenges facing AI/ML applications in healthcare is ensuring equity. It is crucial to provide high-quality healthcare for all populations and that each individual receives care adapted to address the unique challenges of that person. Identifying and minimizing biases within the data is one important method to ensure a fair and effective healthcare system. - Veronica Wallaengen

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ability to assess patients based on their ethnicity, gender, zip, access to resources  helps to provide a more hollistic picture. AI may can be tailored to speicific populations - Yun-Yun Chen

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ability to assess patients based on their ethnicity, gender, zip, access to resources  helps to provide a more hollistic picture. AI may can be tailored to speicific populations - Yun-Yun Chen

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AI can predict outcomes. But over reliance and lack of oversight of an algorithm overtime can lead to poor prediction and potential harm. Something to consider is how often do we retrain and consider new prediction variables? - Yun-Yun Chen

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AI can predict outcomes. But over reliance and lack of oversight of an algorithm overtime can lead to poor prediction and potential harm. Something to consider is how often do we retrain and consider new prediction variables? - Yun-Yun Chen

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Equality is the concept of giving the same resource to different people to reach the same aim, while equity is giving different resources to different people according to their needs so that they can reach the same aim. Equitable data access can influence research outcomes as they would contribute to greater understanding of disease and treatments. For example, suppose that a dataset is only given to one university that specializes in computational resources, but not to a liberal arts college that focuses on the application of social psychology to medicine, for example. Not giving that dataset to one group would prevent qualitative research from being done on the psychosocial effects of chronic disease variables such as pain levels. Research initiatives could be more inclusive by providing data on the justification of the research question and its potential impact, instead of the focus of the research group, their funding or their academic backgrounds. However, these initiatives could also provide training resources to individuals seeking to analyze such data, to ensure that each researcher obtains a fair opportunity to understand and draw conclusions from the data, which would promote equity.

(Asma Sodager)

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The biggest challenge of using AI/ML in healthcare is researching questions on rare genetic conditions; multiple trials on the same participants are needed to obtain a larger dataset.

(Asma Sodager)

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Activity #1: Overcoming AI/ML Challenges in Healthcare

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Activity #1: Overcoming AI/ML Challenges in Healthcare

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One of the challanges that can occur if you are only looking into hospital data that does not provide normal and constant care for patients is that you will have a shewed population against healthy individuals. There is a large portion of the worlds population that dont need to go into the hosptial for years due to good health or only minor concerns.

-Madison Farnsworth

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The biggest challenge is consolidate healthcare data from different resources without compromising patients' privacy.

Yu Liu

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Diverse datasets and inclusive AI design help reduce bias, ensuring that AI-driven healthcare solutions work effectively for all populations. By training models on varied demographics, we create fairer diagnostics and treatment recommendations, leading to better health outcomes for underserved communities.

Anayansi Ramirez

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AI can improve patient care by enabling early disease detection through predictive analytics, leading to faster interventions and better outcomes. However, risks include biased algorithms and misdiagnoses, which could worsen health disparities. Ensuring diverse, high-quality training data and ongoing human oversight can help mitigate these risks while maximizing AI’s potential to enhance healthcare.

Anayansi Ramirez

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Equitable data access ensures diverse perspectives in research, leading to more inclusive and accurate findings. Providing training, shared resources, and partnerships helps underfunded institutions contribute, reducing disparities and improving health outcomes for all.

Anayansi Ramirez

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One major challenge in AI/ML healthcare adoption is bias in training data, which can lead to disparities in patient outcomes. If AI models are trained on non representative datasets, they may provide inaccurate diagnoses or treatments for certain populations. To address this, hospitals should implement diverse, continuously updated datasets.

Anayansi Ramirez

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One of the biggest challenges in using AI/ML in healthcare is data integration and the quality of health data is heterogeneous and affected by biases. Standardized data collection protocols and diverse representative datasets can ensure AI models generalize well across populations.

Sari Mayhue

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One way AI could improve patient care is through early disease detection and diagnosis using machine learning models trained on multimodal data, such as imaging, genomics, and clinical records. This may pose a risk in algorithmic bias, where models trained on non-representative data may lead to disparities in diagnosis and treatment recommendations, potentially worsening healthcare inequalities.

Sari Mayhue

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AI could enable clinicians to provided targeted and personalized care based on each individuals lived experience.  The risks that might come with it is that this would require those building the models ensure they are truly representative of a wide range of groups lived experience.

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AI could enable clinicians to provided targeted and personalized care based on each individuals lived experience.  The risks that might come with it is that this would require those building the models ensure they are truly representative of a wide range of groups lived experience.

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Amirah Ellis-Gilliam

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The biggest challenge in using AI/ML is ensuring that it benefits a wide range of population groups.  AI/ML models need to be trained on a representative population, if not the results will be biased.

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The biggest challenge in using AI/ML is ensuring that it benefits a wide range of population groups.  AI/ML models need to be trained on a representative population, if not the results will be biased.

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Amirah Ellis-Gilliam

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Diverse datasets and inclusive AI can help create a more balanced healthcare system by increasing generalizability and validity in data. By increasing diversity, more patient populations will be represented. Through transparency, clinicians and stakeholders can value input from AI/ML. Bridget Webb

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AI can improve patient care by improving prevention. Prevention is key in healthcare, and AI/ML can detect and identify risk factors or modifiable conditions in patients, as well as those at-risk for certain conditions. Risks of this include under or over-estimating certain populations.   Bridget Webb

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Diverse datasets and inclusive AI design help reduce bias, ensuring AI-driven healthcare solutions are accurate for all populations. This leads to more equitable diagnostics, treatments, and health outcomes, addressing disparities.

-Michael Sam

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I think this approach of representative data should be the standard. For example, including rural areas instead of all urban areas, this will influence findings and potentially identify inequities previously not seen.  Bridget Webb

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AI can improve patient care by enabling early disease detection through predictive analytics, leading to timely interventions. However, risks include biased algorithms that may overlook health disparities in underserved populations, requiring rigorous bias mitigation strategies.

-Michael Sam

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The biggest challenge using AI/ML in healthcare today is transparency. Clinicians need to be able to understand how AI/ML reached a conclusion, thus influencing beneficence.

Bridget Webb

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Equitable data access ensures that underrepresented communities, including Native American populations, are accurately studied, leading to more culturally relevant findings and interventions. Expanding training, partnerships, and computational resources can enhance inclusivity in all health research.

-Michael Sam

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One major challenge is bias in AI/ML models, leading to disparities in patient care. Solution: Implement diverse, representative training data and continuous bias audits to ensure equitable, unbiased healthcare outcomes.

-Michael Sam

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Bridging the Gap with AI/ML & Health Equity

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Bridging the Gap with AI/ML & Health Equity

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Diverse datasets and inclusive AI design are essential to advancing health equity by ensuring that AI technologies are representative of all populations, particularly underserved and vulnerable populations. Incorporating diverse datasets that reflect the spectrum of ethnicity, gender, age, and socio-economic status allows AI systems to better assess the health risks and experiences of historically under-served populations. It also plays a significant role in promoting health equity. Diverse research teams, with varied cultural, gender, and socio-economic perspectives, can develop and test AI models, which are more likely to spot biases in the data or algorithms and correct them. In addition, inclusive AI can uncover health patterns within underrepresented populations, identifying disparities in disease prevalence and/or treatment that would otherwise remain undetected. By integrating diverse data and promoting inclusive design, AI has the potential to not only improve individual care but to advance health equity.

Ali Salman

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Activity #3: AI/ML’s Superpowers in Healthcare

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Activity #3: AI/ML’s Superpowers in Healthcare

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Artificial Intelligence and Machine Learning have promising potential to improve patient care, particularly by enhancing the accuracy and efficiency of diagnoses and treatments. AI algorithms can analyze large quantities of medically relevant data such as medical images, lab results, and patient records more quickly than clinicians. These systems can detect patterns and identify early signs of diseases like cancer, heart disease, or skin disorders. Machine learning models can also predict patient outcomes, allowing for more personalized treatment plans tailored to an individual’s distinctive health needs, which can improve recovery and treatment outcomes. However, the integration of AI into healthcare has its risks. One significant concern is algorithmic bias, where AI models trained on unrepresentative data might lead to biased outcomes, potentially increasing existing healthcare disparities. Under-representation of minority, or rural populations in the training data could result in algorithms that perform poorly for these populations.

Ali Salman

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Bridging the Gap with AI/ML & Health Equity

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Bridging the Gap with AI/ML & Health Equity

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Diverse datasets and inclusive AI design can create a more balanced healthcare system when it includes representativeness. Use of representative data at the outset, and using that data to build and train AI models, helps to ensure that health equity is met. When intentional steps are taken to towards reducing these potential gaps in health outcomes, it makes the data more generalizable to diverse populations.

Making sure that datasets accurately captures and reflect the wider population, be it through diversity in age, education levels, or type of residential area, then the translational science team has done their part to ensure that everyone has an equitable opportunity to be healthy and receive the highest healthcare access possible. - Briana Lettsome

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One way that AI can improve patient care is in a physician’s office during a patient visit. While a physician or nurse is talking with the patient, an AI tool can transcribe the raw audio in the background, whether its recording intake notes or patient summaries, and create clinical notes. As a result, the healthcare provider no longer needs to manually transcribe clinical notes, either during the visit or afterwards, thereby eliminating a time-consuming administrative task and allowing them to fully dialogue and engage with each patient during their visit.

Some potential risks include inconsistencies in transcription which can lead to inaccurate conclusions or diagnoses. Secondly, privacy and confidentiality should always be at the forefront when integrating AI tools in a healthcare system. The selected AI system should be able to maintain patient privacy and healthcare providers should ensure that its use aligns with HIPPA regulations. Lastly, bias is also a consideration. For example, information may be incorrectly transcribed if the AI system was untrained in different language accents. This can also lead to incorrect conclusions on diagnoses or prognoses if the clinician doesn’t recall the conversation with the patient. - Briana Lettsome

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Including multiple datasets from different governing offices allows for a more comprehensive and representative view of healthcare services, ensuring that diverse demographic groups are accurately represented. By integrating data from sources such as government health agencies, insurance companies, and patient registries, researchers can better understand healthcare disparities and identify trends across different populations. This approach provides a holistic view of how healthcare systems serve various communities, offering insights that may not be apparent when using a single dataset. Furthermore, it enables the development of more targeted and effective interventions that address the unique needs of different demographic groups, leading to more equitable healthcare outcomes.

Mirna Elizondo

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One way in which AI can improve patient care is through tailored and personalized modeling. One of the major limitations, especially in spaces like family care in which the ratio of doctor to patient is large, personalized AI healthcare can support providers in looking wore holistically at a patient and providing more data driven and informed care. Whoever one of the downsides is propagated harms and biases. Many systems, due to unbalanced data or poor data quality tend to learn biases and so these bias can propagate into decision making for a personalized model.

Christina Chance

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Diverse datasets and inclusive AI design can create a more balanced healthcare system by ensuring that AI tools are trained on a wide range of patient data, representing different genders, ethnicities, ages, and health conditions. This helps ensure that AI models are more accurate and applicable to everyone, not just certain groups.

Kate Tran

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One way AI can improve patient care is by predicting the likelihood of a patient developing a condition, allowing for early interventions or preventive measures.

However, the risk is that AI tools might be under-representative of certain populations, such as minority or underserved groups. If the data used to train AI models doesn’t include enough diversity, the predictions might not be accurate for these populations, potentially leading to misdiagnosis or missed opportunities for early intervention. This could exacerbate health disparities.

Kate Tran

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Equitable data access ensures that researchers from smaller or underfunded institutions can contribute, leading to more inclusive and accurate findings. By providing tailored support, like training and resources, researchers can focus on issues affecting underserved communities, revealing health disparities that might otherwise be missed. Applying this approach to other health initiatives could broaden perspectives, improve relevance, and ensure that diverse populations are better represented in research outcomes.

Kate Tran

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The biggest challenge in using AI/ML in healthcare today is to develop equity model with high accuracy. To address it, the first thing to consider is representative datasets and then take equity into account when developing the model.

Kate Tran

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By implementing diverse population data, AI can identify the specific needs of these groups, leading to better resource allocation, improved healthcare outcomes, tailored care, all while reducing financial strain.

Aleksandra Leszczynska

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Adaptive Intelligence, combined with Generative AI, is revolutionizing healthcare by continuously learning from data and personalizing treatments for individual patients. This evolving technology not only enhances decision-making but also drives breakthroughs in drug discovery and personalized care.

Aleksandra Leszczynska

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Overcoming Challenges in AI-Driven Healthcare

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Overcoming Challenges in AI-Driven Healthcare

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AI systems must be designed for scalability and adaptability across diverse patient groups and healthcare settings to prevent disparities. Additionally, training healthcare professionals to effectively use AI is essential for its acceptance and integration. Rather than replacing human judgment, AI should serve as a supportive tool that enhances decision-making. By overcoming these challenges, AI can improve diagnostic accuracy, streamline operations, and drive better patient outcomes.

Aleksandra Leszczynska

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AI could improve patient care by helping predict patients at the highest risk of adverse outcomes. One potential risk is the misuse of this information by insurance companies to overcharge or underinsure people at the highest risk of medical issues.

Kelly DuBois

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One of the biggest challenges is creating bias by using an unrepresentative data set for training. To address this, training data sets should be representative of all groups.

Kelly DuBois

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Equitable data access will allow research outcomes to become more representative of the population, allowing hospitals that are not considered to be large research institutions to contribute and benefit the patients that they serve.

Jeong Yun (John) Yang

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The biggest challenge in using AI/ML in healthcare today is transparency/interpretability. For example, we can't know how or the process a cancer prediction model took to arrive at an output. This becomes a barrier for clinicians to adopt those models into real practice.

Jeong Yun (John) Yang

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Training with sufficiently large and diverse datasets

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Training with sufficiently large and diverse datasets

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The utilization of sufficiently large and diverse datasets when training the AI/ML models ensures that underrepresented groups are accurately represented. This would reduce the likelihood of algorithmic bias and thus help enable equitable access to diagnostics, treatments, and preventive care across all population groups.

Mandoye Ndoye

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Challenges in using AI/ML in healthcare today

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Challenges in using AI/ML in healthcare today

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I believe the biggest challenge in this scenario would be how to ensure equity and fairness in the implementation of these AI/ML solutions.  This challenge could be addressed by reducing potential bias in the AI models. This can be achieve by using a diverse datasets, prioritizing transparency via interpretable models that show  equitable care for all populations.

Mandoye Ndoye

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Challenges in Applying AI-ML for Health Equalty

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Challenges in Applying AI-ML for Health Equalty

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Jingchun Chen