Update Blog (Insights) “2025-03-26-how-can-we-…-address-dataset-scarcity-challenges-and-mitigate-algorithmic-bias-to-build-better-ai-healthcare-tools”

Author: Kcl36

_posts/2025-03-26-how-can-we-…-address-dataset-scarcity-challenges-and-mitigate-algorithmic-bias-to-build-better-ai-healthcare-tools.md

@@ -3,8 +3,8 @@ layout: blog-single
 title: How can we … address dataset scarcity challenges and mitigate algorithmic
   bias to build better AI healthcare tools?
 excerpt: >
-  Artificial Intelligence models are only as fair as the data they are trained
-  on. They are vulnerable to biases in training data, often reflecting
+  Artificial Intelligence models are only as fair as the data on which they are
+  trained. They are vulnerable to biases in training data, often reflecting
   deep-rooted systemic inequalities, human prejudices, and socio-economic
   disparities. This is a serious concern in healthcare, where algorithmic
   decisions can directly affect patient outcomes and can lead to severe, even
@@ -30,7 +30,7 @@ Currently, there is limited global representation and diversity in healthcare da
 
 Clinical trials evaluating AI models in healthcare have been driven by countries with large digital economies and populations. [One study](https://www.researchgate.net/publication/375533567_Development_Pipeline_and_Geographic_Representation_of_Trials_for_Artificial_IntelligenceMachine_Learning-Enabled_Medical_Devices_2010_to_2023) found that most clinical trials on AI/ML-enabled devices between 2010 and 2023 were conducted in China (1095 trials), followed by the US (196 trials), Japan (162 trials), while a recent [review](https://www.nature.com/articles/s41746-022-00700-y) of AI use in global health identified only ten studies conducted in resource-limited settings where AI was applied to patient triage, screening, diagnostics, care planning or care provision. Of these, 60% had sample sizes of less than 500 subjects, highlighting significant data availability issues in resource-limited settings.
 
-Overall, the concentration of AI research in healthcare in high-income and upper-middle-income countries presents a significant challenge where harm may be inadvertently caused to patients in resource-limited settings because by AI systems that were not trained on populations like theirs. This can result in deploying AI models which may overlook distinct clinical patterns of disease presentations unique to certain populations, genetic and phenotypic variations, endemic conditions, resource limitations, and region-specific health outcomes, potentially exacerbating health inequalities globally. Though bias can be present in any medical field, its effects are more ‘visible’ in fields like dermatology, where skin conditions can present differently across different skin tones. For example, though melanoma occurs more frequently in lighter-skinner populations, individuals with darker skin can experience higher mortality rate from this cancer. AI systems trained predominantly on one demographic’s data can perform inconsistently when analysing skin conditions in underrepresented groups, thereby perpetuating healthcare disparities. 
+Overall, the concentration of AI research in healthcare in high-income and upper-middle-income countries presents a significant challenge where harm may be inadvertently caused to patients in resource-limited settings  because AI systems are not trained on populations like theirs. This can result in deploying AI models which may overlook distinct clinical patterns of disease presentations unique to certain populations, genetic and phenotypic variations, endemic conditions, resource limitations, and region-specific health outcomes, potentially exacerbating health inequalities globally. Though bias can be present in any medical field, its effects are more ‘visible’ in fields like dermatology, where skin conditions can present differently across different skin tones. For example, though melanoma occurs more frequently in lighter-skinner populations, individuals with darker skin can experience higher mortality rate from this cancer. AI systems trained predominantly on one demographic’s data can perform inconsistently when analysing skin conditions in underrepresented groups, thereby perpetuating healthcare disparities. 
 
 **Exploring solutions through community driven insights and global perspectives**
 
@@ -43,8 +43,7 @@ A publicly available [report](https://doi.org/10.5281/zenodo.14503478) written w
 
 **Mitigating bias**
 
-
-Working with the Accelerate Programme, I will build an open-source virtual hub of resources with Dr Claire Coffey, PhD Candidate in Health Data Science, to improve bias mitigation strategies for healthcare AI deployment from both a clinical and computer science perspective.
+Working with the Accelerate Programme, I am building an open-source virtual hub of resources with Claire Coffey, PhD in Health Data Science, to improve bias mitigation strategies for healthcare AI deployment from both a clinical and computer science perspective.
 
 This virtual platform will offer educational resources to address algorithmic bias and dataset scarcity in healthcare AI development. There will be a contributor-driven model that enables clinicians, computer scientists, data scientists, engineers, and others who use AI in their research to share bias mitigation strategies in pre-processing, in-processing, and post-processing stages. The platform will also feature concise summaries of key topics, such as existing guidelines for assessing presence of bias in clinical studies, toolkits for reducing algorithmic fairness, and carefully curated resource directories – all designed to equip researchers aiming to use AI on healthcare data to develop more equitable healthcare AI systems.