Artificial intelligence (AI) is revolutionizing the face of healthcare in meaningful ways. It is being used to improve the accuracy of diagnoses, streamline patient care planning, and enhance patterns in ongoing monitoring processes. Furthermore, AI can process large medical datasets to find latent patterns and information that clinicians can utilize to make well-informed decisions. AI in healthcare provides sophisticated problem-solving techniques beyond conventional human abilities, permitting a more subtle method towards interpretation, diagnosis, and enabling pioneering, personalized healthcare approaches.

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One area in healthcare AI that is gaining increasing traction is the use of Real-World Data (RWD). Using RWD for the development and AI model training and validation is a prudent tool as it helps capture clinical details in real-world settings, improving accuracy and relevance. And now with growing access to newer real-world data sources like Real-World Imaging Data (RWiD), integration of this data source has also become vital. RWiD adds a further rich layer to data as it captures detailed visual and diagnostic information across imaging modalities like X-rays, MRIs, and CT scans.

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While AI offers tremendous potential in healthcare, it must be deployed thoughtfully and ethically to avoid biases and ensure fair, equitable outcomes.

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Understanding the risk: Bias, Equity Gaps, and Data Diversity Gaps in AI

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When talking about the subject of bias, healthcare AI models may develop biases due to incomplete data and variation in recording or interpreting data. When AI models are trained on biased data, they may inherit and continue with errors and variations, producing discriminatory results and erroneous medical judgments. The model is only as good as the data it has been trained on.

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Secondly, if the training dataset primarily comprises data from one particular demographic or diagnostic group, the AI model learning patterns and features are specific to that group. This model, when implemented in more diverse populations, will fail to perform due to a lack of generalizability. Also, it might make biased predictions since it was not trained on the underrepresented and underdiagnosed groups.

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One other major problem with current AI models is the absence of diversity in training data. When models are trained using limited/homogeneous data, the resulting AI models tend to be biased and may lead to errors, especially for marginalized groups. Healthcare data ethics issues are raised when biases perpetuate stereotypes or result in unequal treatment.

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Though there are continued advancements in healthcare through artificial intelligence, there is growing concern. One big question is whether these innovations are fair and unbiased, or they risk perpetuating already established disparities. As most of the data used for training are from data sources such as clinical trials, specific research studies, frequently lacking equity and data diversity.

All these constraints may lead to suboptimal treatment and diagnostic precision and reduced reliability of healthcare AI models. This highlights the essential need to create AI models that are focused on inclusivity and equity. Training the models with RWD and RWiD serves to resolve the gaps and pitfalls posed by traditional datasets. RWD and RWiD expose models to the heterogeneity of patient populations, rendering them more accurate and generalizable across diverse clinical environments.

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Benefits of using RWD and RWiD for developing ethical AI models 

1. Captures diversity in real-world clinical practice

Real-world data gathered from different healthcare environments, including clinics, hospitals, and non-profit organizations across geographies, encompasses patients from diverse age groups, ethnicities, and socioeconomic statuses. This heterogeneity enables AI models to train on a broad set of clinical situations, allowing the models to learn patterns applicable across different populations. RWiD further captures diversity by reflecting visual disease presentation and patient anatomy differences across populations and care environments. Thus, making informed AI models with a more diverse range of visual clinical evidence.

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2. Improves generalizability and reduces diagnostic disparities

AI models with training from only clinical trial or research study data tend to fail when deployed in real-world settings due to a lack of diversity. Real-world data presents the model to disparate clinical scenarios like patient populations, diseases across geographies, which enables the model to conform to data variations. RWiD places an additional layer on this by enabling AI models to identify robust imaging biomarkers and reliably identify patients in diverse demographics. This leads to reduced disparity and greater reliability.

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3. Enhances fairness in decision-making

AI models trained on RWD are better positioned to recognize health patterns/conditions across all population segments, rather than focusing solely on the majority group. This approach helps to prevent discriminatory or biased outputs. For instance, certain diseases may present differently among various ethnic groups or genders. Since medical imaging guides diagnosis and treatment in nearly all diseases, ranging from oncology and neurodegenerative diseases.  Developing AI models on these diverse RWiD ensures models capture population variations accurately, leading to fairer, more precise clinical decisions.

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4. Facilitates regulatory approval and clinical adoption

RWD supports regulatory compliance by demonstrating that AI models perform reliably across diverse patient populations and clinical settings, reflecting true clinical practice beyond controlled trials. It provides crucial evidence of safety, effectiveness, and generalizability that regulators require for approval.

RWiD further strengthens this by capturing visual clinical nuances and variations in disease presentation across populations, enabling AI models to make accurate diagnoses. Regulatory agencies like the FDA actively encourage the use of RWD for having authorization of multiple AI/ML-enabled medical devices. This underscores the value of RWD and RWiD in meeting regulatory standards and supporting clinical adoption.

Thus, RWD with time has become a key resource in building ethical AI by capturing diverse, real-life clinical information that improves accuracy. Real-world imaging data, as one-step further, adds crucial information captured from various imaging modalities. This enables AI models to detect subtle patterns, identify imaging biomarkers, and better understand variations in disease presentation across diverse patient populations.

Segmed’s role 

Real-world imaging data is not just a technical input but serves as a foundation for ethical, inclusive, and impactful AI in healthcare. By prioritizing diversity in data and mitigating bias, we enable the creation of healthcare AI models that are ethical and reliable. 

At Segmed, we provide access to high-quality, diverse, and de-identified RWiD sourced from a network of healthcare providers, including hospitals, non-profit organizations across different geographies. Our datasets are linked with other data sources like electronic health records (EHRs), claims, and pathology, helping build models that are not only accurate but also equitable and generalizable. Whether you're working on diagnostic algorithms, clinical decision support AI, or AI-based medical device algorithms, Segmed delivers the right, fit-for-purpose data.

Interested in building fair and scalable AI models?

Get in touch with us to explore our datasets or schedule a brief call with us. Let’s build the future of ethical healthcare AI together.

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