Big data and data processing in rheumatology: bioethical perspectives

Area of Science:

• Bioethics
• Health Informatics
• Rheumatology

Background:

• Big data analytics and artificial intelligence (AI) are increasingly integrated into healthcare, including rheumatology.
• This integration presents new opportunities and challenges, particularly concerning sensitive personal health information.

Purpose of the Study:

• To conduct a narrative review of the bioethical perspectives of big data in rheumatology.
• To examine ethical issues related to privacy, confidentiality, informed consent, professional impact, and justice in the context of AI and big data applications in rheumatology.

Main Methods:

• Review of current applications of big data, data analytics, and AI in rheumatology.
• Analysis of examples including databases, machine learning algorithms, and consumer-facing platforms.
• Discussion of four main ethical issues: privacy/confidentiality, informed consent, impact on the medical profession, and justice.

Main Results:

• Big data and AI in rheumatology can enhance diagnosis, treatment, and prognosis, potentially improving patient care and engagement.
• Ethical challenges include ensuring privacy in registries, maintaining physician-patient fiduciary obligations with AI-driven insights, and addressing potential health inequalities.
• International collaboration and patient engagement are identified as strategies to promote equity.

Conclusions:

• Responsible development and ethical application of big data and AI in rheumatology can significantly improve healthcare quality and democratize health.
• Addressing bioethical concerns is paramount to realizing the full potential of these technologies.
• Strategies like international collaboration and patient engagement can help mitigate inequalities and advance global health policy.