ข้อมูลเชิงลึก - Healthcare - # Dermatology Image Crowdsourcing

Crowdsourcing Dermatology Images with Google Search Ads: Creating a Real-World Skin Condition Dataset

Q: How can crowdsourced datasets like SCIN be utilized beyond dermatology?

Crowdsourced datasets like SCIN can be leveraged in various ways across different healthcare domains. These datasets can serve as valuable resources for medical education, providing diverse and representative images for textbooks, atlases, and online educational materials. Additionally, they can support training programs for medical students, residents, and practicing providers by offering real-world examples of common health conditions. In the realm of health and population research, these datasets enable the modeling and monitoring of seasonal trends and outbreaks of dermatological or other health conditions. Researchers can also explore the prevalence of dermatology conditions within communities compared to those seen in healthcare systems. Furthermore, these datasets provide insights into skin type and skin tone representation in dermatology practice. For consented population-based health dataset creation, crowdsourced data could be instrumental in establishing rare disease registries or collecting continuous health metrics such as glucose levels or heart rate using fitness applications or phone sensors. They could also capture environmental signals impacting health like air quality or water quality. In terms of AI research and development, crowdsourced datasets offer opportunities to evaluate the accuracy and fairness of AI models trained on diverse data sources. They facilitate testing model generalizability across different populations while aiding in self-supervised model training or generative model development. Moreover, researchers can train models specifically for skin tone or skin type classification using these datasets.

Q: What potential biases or limitations exist in using search ads for dataset creation?

While utilizing search ads for dataset creation offers numerous advantages such as broad reach and accessibility to a diverse pool of contributors, there are potential biases and limitations associated with this approach: Selection Bias: Search ad campaigns may attract individuals who are more tech-savvy or have greater internet access than others. Demographic Biases: The demographics represented in the dataset may not fully reflect the broader population due to differential rates of internet usage among various demographic groups. Self-Selection Bias: Individuals who choose to participate may have specific motivations that differ from those who do not contribute. Quality Control Challenges: Ensuring image quality standards without direct supervision poses challenges that could impact data reliability. Privacy Concerns: Despite efforts to de-identify images during processing stages, there is always a risk associated with privacy breaches when handling personal information through online platforms.

Q: How might the inclusion of demographic information impact fairness and equity in healthcare AI tools?

The inclusion of demographic information in healthcare AI tools has both positive implications for fairness and equity as well as potential challenges: Fairness Enhancement: By incorporating demographic data into AI algorithms used within healthcare settings, developers can better account for disparities related to race/ethnicity, gender identity etc., ensuring fairer outcomes across diverse patient populations. Equity Considerations: Understanding how certain demographics interact with specific treatments allows practitioners to tailor interventions based on individual needs, promoting equitable access to high-quality care regardless socio-economic status However, Potential Pitfalls: Over-reliance on demographic factors alone risks reinforcing stereotypes perpetuating bias rather than mitigating it; therefore careful consideration must be given regarding how this information is integrated into algorithmic decision-making processes Privacy Concerns: Collecting sensitive demographic details raises concerns about patient privacy protection especially if mishandled leading unintended consequences including discrimination Overall ,the thoughtful integration demograhic variables holds promise enhancing effectiveness equitability Healthcare A.I Tools while necessitating vigilance against pitfalls inherent capturing sensitive attributes

แนวคิดหลัก

The author utilizes Google Search ads to crowdsource dermatology images, creating a diverse and representative dataset of skin conditions.

บทคัดย่อ

The study focuses on using search ads to gather images of dermatology conditions from internet users in the US. The dataset contains 10,408 images from 5,033 contributors over 8 months. It highlights the effectiveness of search ads in crowdsourcing health data and addresses gaps in existing datasets by including common skin conditions. The method allows for diverse representation across demographics and skin types, providing valuable resources for research, education, and AI tool development.

สถิติ

We received a median of 22 submissions/day (IQR 14–30).
Female contributors had higher representation at 66.72%.
Over 97.5% of contributions were genuine images of skin conditions.
Most contributions were short-duration (54% with onset < 7 days ago).
eFST and eMST distributions reflected the geographical origin of the dataset.

คำพูด

"Search ads are effective at crowdsourcing images of health conditions."
"The SCIN dataset bridges important gaps in the availability of representative images of common skin conditions."

ข้อมูลเชิงลึกที่สำคัญจาก

Crowdsourcing Dermatology Images with Google Search Ads

by Abbi Ward,Ji... ที่ arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18545.pdf

Crowdsourcing Dermatology Images with Google Search Ads

สอบถามเพิ่มเติม

How can crowdsourced datasets like SCIN be utilized beyond dermatology?

Crowdsourced datasets like SCIN can be leveraged in various ways across different healthcare domains. These datasets can serve as valuable resources for medical education, providing diverse and representative images for textbooks, atlases, and online educational materials. Additionally, they can support training programs for medical students, residents, and practicing providers by offering real-world examples of common health conditions.
In the realm of health and population research, these datasets enable the modeling and monitoring of seasonal trends and outbreaks of dermatological or other health conditions. Researchers can also explore the prevalence of dermatology conditions within communities compared to those seen in healthcare systems. Furthermore, these datasets provide insights into skin type and skin tone representation in dermatology practice.
For consented population-based health dataset creation, crowdsourced data could be instrumental in establishing rare disease registries or collecting continuous health metrics such as glucose levels or heart rate using fitness applications or phone sensors. They could also capture environmental signals impacting health like air quality or water quality.
In terms of AI research and development, crowdsourced datasets offer opportunities to evaluate the accuracy and fairness of AI models trained on diverse data sources. They facilitate testing model generalizability across different populations while aiding in self-supervised model training or generative model development. Moreover, researchers can train models specifically for skin tone or skin type classification using these datasets.

What potential biases or limitations exist in using search ads for dataset creation?

While utilizing search ads for dataset creation offers numerous advantages such as broad reach and accessibility to a diverse pool of contributors, there are potential biases and limitations associated with this approach:

Selection Bias: Search ad campaigns may attract individuals who are more tech-savvy or have greater internet access than others.

Demographic Biases: The demographics represented in the dataset may not fully reflect the broader population due to differential rates of internet usage among various demographic groups.

Self-Selection Bias: Individuals who choose to participate may have specific motivations that differ from those who do not contribute.

Quality Control Challenges: Ensuring image quality standards without direct supervision poses challenges that could impact data reliability.

Privacy Concerns: Despite efforts to de-identify images during processing stages, there is always a risk associated with privacy breaches when handling personal information through online platforms.

How might the inclusion of demographic information impact fairness and equity in healthcare AI tools?

The inclusion of demographic information in healthcare AI tools has both positive implications for fairness
and equity as well as potential challenges:

Fairness Enhancement: By incorporating demographic data into AI algorithms used within healthcare settings,
developers can better account for disparities related to race/ethnicity,
gender identity etc., ensuring fairer outcomes across diverse patient populations.

Equity Considerations: Understanding how certain demographics interact with specific treatments
allows practitioners to tailor interventions based on individual needs,
promoting equitable access to high-quality care regardless
socio-economic status

However,

Potential Pitfalls: Over-reliance on demographic factors alone risks reinforcing stereotypes
perpetuating bias rather than mitigating it; therefore careful consideration must be given
regarding how this information is integrated into algorithmic decision-making processes

Privacy Concerns: Collecting sensitive demographic details raises concerns about patient privacy
protection especially if mishandled leading unintended consequences including discrimination

Overall ,the thoughtful integration  demograhic variables holds promise enhancing effectiveness
equitability Healthcare A.I Tools while necessitating vigilance against pitfalls inherent capturing sensitive attributes

Crowdsourcing Dermatology Images with Google Search Ads: Creating a Real-World Skin Condition Dataset