Report on the Role of Artificial Intelligence in Advancing the Sustainable Development Goals

Introduction: Aligning AI with Global Health and Innovation Mandates

Artificial Intelligence (AI), a field with a 75-year history, has recently entered mainstream discourse, causing both intrigue and apprehension. Despite its long existence, a general lack of understanding persists. The capabilities of AI, particularly in processing large-scale information (Big Data) with unwavering accuracy, surpass human cognitive limits, making its integration into various sectors inevitable. The World Economic Forum has specifically called for an accelerated adoption of AI in healthcare to drive progress and innovation, directly supporting Sustainable Development Goal 3 (Good Health and Well-being) and SDG 9 (Industry, Innovation, and Infrastructure). However, the uptake has been slower than anticipated due to barriers such as policy complexity and fragmented regulations, which can lead to misaligned technical and strategic decisions. This report surveys the current landscape of AI, highlighting its applications in clinical work and research and its potential to accelerate the achievement of the Sustainable Development Goals.

Technological Evolution and its Contribution to SDG 9: Industry, Innovation, and Infrastructure

From Rule-Based Systems to Autonomous Learning

The technological infrastructure underpinning AI has evolved significantly since its inception. The journey from primitive, stationary computers to modern laptops, smartphones, and wearable sensor networks laid the groundwork for widespread AI application. A pivotal shift occurred due to the demands of the gaming industry for massively parallel computing, which spurred the transition from central processor units (CPUs) to specialized graphics processors (GPUs) and tensor processors (TPUs). This advancement in hardware architecture enabled the development of sophisticated neural networks, machine learning, and meta-learning algorithms. The emergence of generative, pre-trained transformers (GPTs) further demonstrated AI’s capacity to understand and respond to human language. This evolution from rigid, rule-based computing to flexible systems that can adapt to new information and changing environments represents a significant leap in technological innovation, a core component of SDG 9.

Agent-Based AI and the Internet of Things (IoT)

A further advancement is agent-based AI, where autonomous agents perceive their environment, make decisions, and act to achieve goals. This decentralized approach, where multiple agents can collaborate, allows for the simulation and analysis of complex adaptive systems. Its potential applications are vast and directly support several SDGs:

• Disease Transmission Dynamics (SDG 3): Modeling the spread of diseases, including Neglected Tropical Diseases (NTDs), to develop effective control strategies.
• Complex System Analysis (SDG 9 & 11): Studying phenomena such as traffic flow and social polarization to inform urban planning and policy.
• The Internet of Things (IoT): Agent-based principles are also seen in IoT, where embedded sensors and software collect and exchange data, enabling smarter resource management and monitoring, which contributes to sustainable infrastructure.

This shift from predictable, rule-based outcomes to a model incorporating learning and reasoning allows for non-human choice, providing powerful new tools for research and problem-solving.

AI Applications in Support of SDG 3: Good Health and Well-being

Enhancing Clinical Diagnostics and Treatment

AI’s application in clinical settings is revolutionizing healthcare delivery and contributing directly to achieving SDG 3. Early examples, such as the MYCIN system for diagnosing bacterial infections, have paved the way for today’s advanced applications. The impact is particularly notable in image-based diagnostics, where AI enhances accuracy and efficiency.

1. Medical Imaging: AI is used for interpreting a wide range of diagnostic images, including microscopy for parasitology and cancer detection, electrocardiography (EKG), computer tomography (CT), magnetic resonance imaging (MRI), and ultrasonography.
2. Commercial Health Solutions: Several companies are successfully deploying AI. ACL Digital analyzes data from wearable sensors to detect conditions like heart arrhythmias, while AIdoc’s platform evaluates clinical examinations to coordinate workflows. The da Vinci Surgical System uses AI to assist in complex procedures, improving patient outcomes.
3. Regulatory Challenges: The failure of ambitious projects like IBM’s ‘Watson for Oncology’ and the tele-health service Babylon Health underscores the critical need for strong regulation to ensure patient safety and efficacy before AI-assisted medical tools are released for public use.

Accelerating Basic Research and Drug Discovery

AI is a transformative force in basic scientific research, accelerating discoveries that are fundamental to improving human health. The development of AlphaFold, an AI system that solved the longstanding problem of protein folding, has opened new frontiers in biology and medicine. This breakthrough enables researchers to design novel proteins, accelerate drug discovery, and better understand how genetic mutations lead to disease. The subsequent version, AlphaFold3, models interactions between proteins and other cellular components, providing deeper insights into biological processes. Furthermore, AI’s ability to analyze Big Data has been proven to enhance diagnostic reliability. A study involving Google Research demonstrated that a machine learning system reduced false positives in mammography screening by 25% without missing any true positives, showcasing AI’s potential to improve healthcare quality and efficiency in line with SDG 3.

Strengthening Epidemiological Surveillance and Disease Control

AI tools are critical for modern epidemiological surveillance, particularly for controlling infectious diseases. By processing Big Data from diverse sources, AI can identify patterns and predict outbreaks, which is essential for public health preparedness.

• One Health/Planetary Health: AI facilitates the integration of human, animal, and environmental health data. This approach is vital for understanding and managing zoonotic and parasitic diseases, optimizing resource management, and promoting proactive synergies between disciplines like ecology, genomics, and socio-economy.
• Elimination of Neglected Tropical Diseases (NTDs): AI is instrumental in advancing the World Health Organization’s (WHO) 2030 roadmap for NTD elimination. The Expanded Special Project for the Elimination of Neglected Tropical Diseases (ESPEN) utilizes a portal with AI-driven planning tools. By analyzing satellite imagery, climate data, and historical disease patterns, the platform identifies high-risk areas for targeted interventions, making the fight against NTDs more data-driven, precise, and scalable, thereby accelerating progress towards a key target within SDG 3.

Interlinkages with Other SDGs: Climate Action and Global Partnerships

AI in Monitoring Climate-Sensitive Diseases (SDG 13)

The surveillance of vector-borne diseases offers a direct link between AI applications and SDG 13 (Climate Action). Arthropod vectors are highly sensitive to changes in temperature and precipitation, making them key indicators of climate change. By analyzing complex interactions between climate variables, ecosystems, and disease life cycles, AI technologies provide crucial insights into the health impacts of a changing climate. This enables public health systems to anticipate and respond to shifts in disease distribution, contributing to climate adaptation strategies.

Fostering Collaborative Efforts for Global Health (SDG 17)

The effective deployment of AI for global health relies on robust collaborations, reflecting the principles of SDG 17 (Partnerships for the Goals). The ESPEN initiative is a prime example, representing a collaborative effort between the WHO, African member states, and global NTD partners. Similarly, research breakthroughs are increasingly the result of public-private partnerships, such as those between academic institutions, clinicians, and technology companies like Google. The One Health approach itself is built on a foundation of interdisciplinary and cross-sectoral collaboration, demonstrating that partnerships are essential to harnessing AI’s full potential for sustainable development.

Conclusion: The Proliferation of AI Research and its Implications for the SDGs

The volume of research on AI has grown exponentially, indicating its increasing importance across all sectors. The total number of general AI publications rose from approximately 88,000 in 2010 to over 240,000 in 2022. A significant portion of this growth is in health-related fields. A PubMed search for “AI and infectious disease(s)” yields nearly 100,000 entries, dominating the overall AI research landscape. This surge in publications, particularly on machine learning applications in medicine and surveillance, highlights the scientific community’s recognition of AI’s potential to solve pressing global health challenges. This trend confirms that AI is not merely a tool for innovation but a critical enabler for achieving the Sustainable Development Goals, especially SDG 3, by making healthcare more precise, efficient, and scalable.

Identified Sustainable Development Goals (SDGs)

• SDG 3: Good Health and Well-being – The article extensively discusses the application of AI in healthcare, from diagnostics and treatment to epidemiological surveillance and basic medical research.
• SDG 9: Industry, Innovation, and Infrastructure – The text focuses on technological advancement (AI, GPUs, TPUs, IoT), innovation in research (AlphaFold), and the infrastructure required to support these technologies.
• SDG 13: Climate Action – The article links AI-driven epidemiological surveillance to monitoring vector-borne diseases that are influenced by climate change.
• SDG 17: Partnerships for the Goals – The article highlights the importance of collaboration between countries, international organizations (WHO), academic institutions, and private companies (Google) to advance AI in health and share data.

Specific SDG Targets

SDG 3: Good Health and Well-being

• Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases.
  • The article explicitly mentions using agent-based AI to “successfully control the neglected tropical diseases (NTDs)” and references the WHO’s “roadmap for elimination of the NTDs.” It also discusses using AI to study the “spread of disease” and for epidemiological surveillance of infectious and parasitic diseases.
• Target 3.b: Support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries.
  • The article highlights how AI, specifically AlphaFold2, has solved the protein-folding problem, which can “accelerate drug discovery” and help “design novel proteins with specific functions (synthetic biology).” This directly supports the R&D of new medicines.
• Target 3.d: Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks.
  • The text describes how AI is used for “epidemiological surveillance,” tracing disease outbreaks, and making “accurate predictions of disease transmission.” The ESPEN portal is cited as an example of using satellite and climate data to “identify high-risk areas for targeted interventions.”

SDG 9: Industry, Innovation, and Infrastructure

• Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries… encouraging innovation and substantially increasing the number of research and development workers… and public and private research and development spending.
  • The article details the technological evolution from CPUs to GPUs and TPUs, which fueled the expansion of AI. It emphasizes AI’s role in basic research (protein folding) and notes the exponential growth in AI-related publications, from “88,000 in 2010 to more than 240,000 in 2022,” indicating a substantial increase in research and innovation.

SDG 13: Climate Action

• Target 13.3: Improve education, awareness-raising and human and institutional capacity on climate change mitigation, adaptation, impact reduction and early warning.
  • The article states that arthropod vectors of disease are “bellwether indicators… for climate change.” It explains that AI assists in understanding the “complex interactions between climate, ecosystems and parasitic diseases,” thereby improving institutional capacity for early warning and adaptation to the health impacts of climate change.

SDG 17: Partnerships for the Goals

• Target 17.6: Enhance North-South, South-South and triangular regional and international cooperation on and access to science, technology and innovation and enhance knowledge-sharing.
  • The article mentions the collaborative nature of AI research and application, citing the “Expanded Special Project for the Elimination of Neglected Tropical Diseases (ESPEN)” as a joint effort between the WHO, African member states, and partners. It also notes that China, India, and the United States are particularly active in AI research, implying global cooperation and competition.
• Target 17.16: Enhance the Global Partnership for Sustainable Development, complemented by multi-stakeholder partnerships that mobilize and share knowledge, expertise, technology and financial resources.
  • The text points to “proactive synergies between public health and other disciplines, such as ecology, genomics, proteomics, bioinformatics, sanitary engineering and socio-economy.” It also mentions a partnership between clinicians and Google Research on a diagnostic AI system, exemplifying a multi-stakeholder partnership.
• Target 17.18: By 2020, enhance capacity-building support to developing countries… to increase significantly the availability of high-quality, timely and reliable data disaggregated by income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts.
  • The article discusses the necessity of handling “Big Data” in healthcare and epidemiology. It mentions the need for “data-sharing at the local level but also nationally and globally” and how AI-driven platforms can “facilitate real-time information exchange between stakeholders,” which is essential for increasing the availability of timely and reliable data.

Implied or Mentioned Indicators

SDG 3: Good Health and Well-being

• For Target 3.3:
  • The use of AI models to monitor and control specific diseases like schistosomiasis and other NTDs. Progress on the WHO’s “roadmap for neglected tropical diseases 2021–2030” is an implied indicator.
• For Target 3.b:
  • The development of tools like AlphaFold and the subsequent design of novel proteins and acceleration of drug discovery serve as indicators of progress in R&D.
• For Target 3.d:
  • Reduction in false positives in diagnostics (e.g., a “25% reduction in false positives in a large mammography dataset”).
  • Deployment of AI-driven surveillance platforms like the ESPEN portal that use satellite and climate data for prediction.

SDG 9: Industry, Innovation, and Infrastructure

• For Target 9.5:
  • The number of AI-related scientific publications, which the article states rose from “approximately 88,000 in 2010 to more than 240,000 in 2022.”
  • The development and adoption of new technologies like GPUs, TPUs, and agent-based AI systems.

SDG 13: Climate Action

• For Target 13.3:
  • The application of AI tools to analyze the link between climate variables (temperature, precipitation) and the distribution of disease vectors.

SDG 17: Partnerships for the Goals

• For Target 17.6 & 17.16:
  • The existence of collaborative platforms and projects, such as the ESPEN portal and joint academic-corporate research papers (clinicians and Google Research).
• For Target 17.18:
  • The use of AI to manage and analyze “Big Data repositories” for healthcare and the establishment of platforms for “real-time information exchange.”

Summary Table of SDGs, Targets, and Indicators

Source: idpjournal.biomedcentral.com