Does an App a Day Keep the Doctor Away? AI Symptom Checker Applications, Entrenched Bias, and Professional Responsibility
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View abstract on PubMed
Summary
This summary is machine-generated.AI-powered symptom checker apps offer potential health access but raise concerns about bias and accountability. Addressing these ethical and legal gaps is crucial for safe mHealth development and use.
Area Of Science
- Digital Health
- Artificial Intelligence in Healthcare
- Medical Ethics
Background
- Artificial intelligence (AI) is increasingly used in mobile health (mHealth), leading to AI-powered symptom checker apps (AISympCheck).
- These apps present ethical and legal challenges, including bias entrenchment and professional accountability issues.
- The COVID-19 pandemic highlighted the widespread use and potential impact of AISympCheck apps like Babylon and Ada.
Purpose Of The Study
- To analyze the ethical and legal concerns associated with AI-powered symptom checker apps.
- To examine how bias is entrenched in AI systems and its impact on healthcare inequalities.
- To investigate the challenges in professional accountability and liability for app-mediated misdiagnoses.
Main Methods
- Focused analysis on two prominent mHealth apps, Babylon and Ada, as case studies.
- Examined the "garbage in, garbage out" phenomenon in AI training data and its effect on bias.
- Assessed the regulatory landscape and professional guidance for AISympCheck apps.
Main Results
- Bias in AISympCheck apps often stems from unrepresentative training data, perpetuating health disparities.
- Lack of regulation and clear guidelines creates challenges for professional accountability and physician recommendations.
- Users may receive distorted health assessments due to demographic biases in app data.
Conclusions
- Urgent consideration of legal and ethical issues is needed for the design and implementation of AISympCheck apps.
- Professional bodies must play a key role in developing guidance to address ethical and legal gaps.
- Technical safeguards, neutral data training, and regulatory oversight are essential to mitigate bias and ensure informed user decisions in mHealth.
Keywords:
app application applications apps artificial intelligence bias biases diagnose diagnosis ethic ethical ethics law laws legal mHealth mobile health mobile phone professional obligation professional obligations regulation regulations safety self-diagnose self-diagnosis symptom checker symptom checkers
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