AI in neurological care could widen health inequities, new report warns – News-Medical

Report on the Implementation of Artificial Intelligence in Neurological Care and its Alignment with Sustainable Development Goals

Executive Summary

A comprehensive report, co-authored by UCLA Health and published in Neurology, examines the expanding role of Artificial Intelligence (AI) in neurological care. The findings indicate that while AI presents significant opportunities to advance Sustainable Development Goal 3 (SDG 3: Good Health and Well-being), its implementation poses considerable risks to Sustainable Development Goal 10 (SDG 10: Reduced Inequalities). The report establishes guiding principles to ensure AI development is equitable, accountable, and contributes positively to global health objectives.

AI’s Role in Advancing SDG 3: Good Health and Well-being

AI technology demonstrates substantial potential to enhance health outcomes and support the achievement of universal health coverage as outlined in SDG 3. Current and potential applications in neurology include:

• Accelerating diagnostic processes, such as the classification of brain tumors and the analysis of stroke imaging.
• Empowering healthcare providers in resource-limited settings, thereby addressing shortages of specialists like neurologists.
• Enabling earlier detection of neurological diseases through the analysis of clinical notes.
• Improving health system efficiency to ensure all patient groups receive high-quality care.

Challenges to SDG 10: Reduced Inequalities

The report highlights a critical challenge: the potential for AI to exacerbate existing health disparities, directly contravening SDG 10. This risk stems from AI’s reliance on large datasets that often underrepresent vulnerable and underdiagnosed populations.

Conversely, if developed with equity as a foundational principle, AI can be a powerful tool for reducing inequalities. Potential applications to promote health equity include:

• Assisting physicians in areas with neurologist shortages to recognize diseases months earlier.
• Ensuring prescribed medications align with patient affordability.
• Automatically translating medication instructions into a patient’s primary language.
• Identifying and flagging the systematic exclusion of certain populations from clinical trials.

Guiding Principles for Equitable AI Implementation

To align the deployment of AI in healthcare with the Sustainable Development Goals, the report outlines three core principles, reflecting the need for inclusive innovation (SDG 9), strong institutions (SDG 16), and multi-stakeholder partnerships (SDG 17).

1. Inclusive Development and Diverse Perspectives

   To meet the objectives of SDG 10, AI development must be shaped by diverse perspectives. Healthcare institutions should engage community advisory boards that reflect patient demographics to ensure AI tools are culturally sensitive, linguistically appropriate, and do not perpetuate bias.

2. AI Education for Healthcare Professionals

   In support of SDG 3, neurologists and other healthcare professionals must receive training to understand AI’s limitations. This education should focus on recognizing potential biases in algorithmic outputs to ensure AI serves as a supplementary tool, not an infallible authority.

3. Strong Governance and Accountability

   Achieving SDG 16 requires the establishment of strong governance frameworks for AI in healthcare. This includes independent oversight with clear accountability to monitor AI performance, investigate failures, and empower patients with rights, such as the ability to report concerns or request the deletion of their health data.

Conclusion

The development and deployment of AI in healthcare is at a critical juncture. The decisions made by regulators, healthcare institutions, developers, and patients will determine whether this technology advances global goals for health and equality. A collaborative approach, grounded in principles of equity and accountability, is essential to ensure AI becomes a force for achieving the Sustainable Development Goals rather than a new barrier to care.

Analysis of SDGs in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

   The article on the use of Artificial Intelligence (AI) in neurological care connects to several Sustainable Development Goals (SDGs) by focusing on health outcomes, equity, and the governance of new technologies.

   • SDG 3: Good Health and Well-being: This is the primary SDG addressed. The article is centered on healthcare, specifically “neurological care,” and discusses how AI can be used to “detect strokes or seizures,” “classify brain tumors,” and “improve health outcomes.”
   • SDG 10: Reduced Inequalities: This is a central theme of the article. It explicitly warns that AI “could also worsen health disparities” and emphasizes the need to “build it with equity as the foundation.” The discussion revolves around ensuring that AI benefits all populations, including “vulnerable populations who are already underrepresented in research.”
   • SDG 16: Peace, Justice and Strong Institutions: The article calls for robust governance frameworks to manage AI in healthcare. The recommendation for “strong governance,” “independent oversight with clear accountability,” and giving patients the ability to “report concerns” directly relates to building effective and accountable institutions.
   • SDG 9: Industry, Innovation and Infrastructure: The article discusses the implementation of a cutting-edge technology (AI) within the healthcare industry. It focuses on harnessing this innovation to “allow doctors to make faster decisions” and improve care, which aligns with upgrading technological capabilities and encouraging innovation.

2. What specific targets under those SDGs can be identified based on the article’s content?

   Several specific SDG targets can be identified from the article’s discussion on equitable AI implementation in healthcare.

   • Target 3.8 (under SDG 3): Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all. The article supports this target by describing how AI can help “healthcare providers in resource-limited settings,” “ensure medications match what patients can afford,” and “ensure all patient groups are receiving high quality care.”
   • Target 3.d (under SDG 3): Strengthen the capacity of all countries… for early warning, risk reduction and management of national and global health risks. The potential for AI to help doctors “recognize early signs of neurological diseases months earlier” directly contributes to this target by improving early warning and risk reduction at the patient level.
   • Target 10.2 (under SDG 10): By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status. The article addresses this by highlighting the risk of AI worsening disparities for “vulnerable populations” and calling for measures to “improve enrollment of underrepresented groups in research studies” and flag when “certain populations are being systematically excluded from clinical trials.”
   • Target 10.3 (under SDG 10): Ensure equal opportunity and reduce inequalities of outcome… The article’s core message is to prevent AI from creating unequal health outcomes. The call to build AI with “equity as the foundation” is aimed at achieving this target.
   • Target 16.6 (under SDG 16): Develop effective, accountable and transparent institutions at all levels. This is reflected in the guiding principle for “strong governance” which demands “independent oversight with clear accountability” to “monitor AI performance” and “investigate failures.”
   • Target 16.7 (under SDG 16): Ensure responsive, inclusive, participatory and representative decision-making at all levels. The principle that “Diverse perspectives must shape AI development” by involving “community advisory boards reflecting the demographics of populations they serve” is a direct call for inclusive and participatory decision-making.
   • Target 9.5 (under SDG 9): Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries…encouraging innovation… The entire article is about leveraging a major technological innovation (AI) to upgrade the capabilities of the healthcare sector.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

   The article does not mention official SDG indicators, but it implies several metrics that could be used to measure progress towards the identified targets.

   • Indicator for Target 3.8: The article implies measuring the rate of early detection of neurological diseases in resource-limited settings where AI tools are deployed. Another indicator would be the measurement of health outcomes across different demographic and socioeconomic groups to ensure “all patient groups are receiving high quality care.”
   • Indicator for Target 10.2: Progress could be measured by the percentage of clinical trials using AI that meet diversity and inclusion benchmarks, addressing the concern of “underrepresented groups.” Another indicator is the number of languages and cultural adaptations available in AI-driven patient tools, such as the mentioned ability to “automatically write medication instructions in the patient’s primary language.”
   • Indicator for Target 16.6: An implied indicator is the establishment of independent oversight bodies for AI in healthcare. Progress could also be tracked by the number of patient-reported concerns about AI that are formally investigated, reflecting the call for accountability.
   • Indicator for Target 16.7: A direct indicator from the article is the number and representativeness of community advisory boards involved in the development and oversight of healthcare AI tools.

SDGs, Targets and Indicators Summary

Source: news-medical.net