Report on the Application of Artificial Intelligence in Healthcare to Advance Sustainable Development Goals

Executive Summary

This report details a case study wherein a patient utilized a personally developed Artificial Intelligence (AI) system to overcome a diagnostic failure within the conventional healthcare system. The case highlights the potential of AI to significantly contribute to the achievement of several Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 17 (Partnerships for the Goals). The patient’s experience demonstrates how AI can enhance diagnostic accuracy, personalize treatment, and empower individuals, thereby addressing systemic limitations in healthcare delivery.

Case Background and Initial Diagnostic Challenges

A 60-year-old male presented with symptoms including weight loss, fatigue, and abdominal discomfort. Despite undergoing extensive medical evaluations, the initial diagnosis was limited to mild gastritis, with suggestions of stress or depression as potential causes. The underlying condition, an aggressive form of blood cancer related to multiple myeloma, was missed.

• Initial medical investigations included full body scans, colonoscopy, endoscopy, and cardiac function tests.
• A subsequent emergency room visit at a different health system led to the correct diagnosis, revealing the cancer was affecting bone marrow, kidneys, gut, and heart.
• The initial diagnostic failure underscores systemic challenges in managing complex cases within overburdened healthcare systems, a key barrier to achieving universal health coverage as outlined in SDG 3.

AI Integration for Enhanced Health Outcomes (SDG 3)

To address the diagnostic gap, the patient developed a medical AI agent, “Haley,” which analyzed his complete medical history. This application of technology directly supports SDG Target 3.4, which aims to reduce premature mortality from non-communicable diseases through prevention and treatment.

Improving Diagnostic Accuracy

The AI agent processed the same data available to the initial physicians but yielded new, critical insights.

1. The AI identified a pattern of mild anemia, elevated ferritin, and low immunoglobulins, indicating immune dysfunction.
2. It recommended specific tests, including a serum free light chains blood test and a bone marrow biopsy, which had not been previously suggested.
3. This demonstrates AI’s capacity to synthesize complex data and identify subtle signals, leading to earlier and more accurate diagnoses.

Personalizing Treatment and Patient Empowerment

Following diagnosis, AI was utilized to move beyond standard-of-care protocols and develop a personalized treatment strategy.

• The AI analyzed the patient’s cytogenetics report, identifying genetic variants that could render standard treatments less effective.
• It cross-referenced these mutations with global clinical literature to suggest a more aggressive, off-label combination therapy (Daratumumab plus Venetoclax).
• This personalized approach resulted in an exceptional response, with the patient’s key cancer marker returning to the normal range.
• By enabling the patient to become an active, informed participant in his care, this model promotes the shared decision-making essential for patient-centered healthcare under SDG 3.

Fostering Innovation and Resilient Infrastructure (SDG 9)

The development and application of the AI system, named CureWise, exemplifies the innovation required to meet SDG 9. This goal emphasizes upgrading technological capabilities and enhancing scientific research to build resilient infrastructure.

Technological Advancement in Healthcare

The case represents a significant innovation in patient-led health management.

1. A virtual multidisciplinary team of AI agents (oncologist, hematologist, etc.) was created to provide diverse specialist perspectives.
2. The system’s ability to process vast amounts of medical research efficiently overcomes the human limitation of keeping pace with scientific discovery.
3. This technology serves as a model for augmenting the capabilities of both patients and clinicians, making the healthcare ecosystem more intelligent and responsive.

Strengthening Partnerships for the Goals (SDG 17)

The success of this case was predicated on a new collaborative model, aligning with the principles of SDG 17, which calls for multi-stakeholder partnerships to achieve sustainable development.

A New Collaborative Model for Care

The AI did not replace human medical professionals but rather facilitated a more effective partnership.

• The patient used AI-generated insights to seek consultations with human specialists at leading cancer centers.
• This created a collaborative dynamic where AI-powered analysis was validated and implemented by expert clinicians.
• The patient’s new medical team has been open to the AI’s role, viewing it as a tool for shared decision-making.

Conclusion

This case study provides compelling evidence that AI can serve as a transformative tool in advancing global health objectives. By augmenting human intelligence, AI can help overcome systemic healthcare barriers, leading to earlier diagnoses, highly personalized treatments, and empowered patients. The integration of such technologies fosters innovation (SDG 9) and creates new collaborative partnerships (SDG 17), ultimately driving progress toward ensuring good health and well-being for all (SDG 3).

Analysis of Sustainable Development Goals in the Article

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

The article primarily addresses two Sustainable Development Goals (SDGs) based on its focus on health, healthcare systems, and the use of technology to improve medical outcomes.

• SDG 3: Good Health and Well-being: The entire narrative revolves around the author’s personal health journey, from experiencing symptoms and seeking a diagnosis for a serious illness (an aggressive blood cancer) to undergoing treatment. It critiques the existing healthcare system’s ability to provide timely and accurate care and explores ways to improve patient outcomes.
• SDG 9: Industry, Innovation, and Infrastructure: The article heavily emphasizes the role of technological innovation, specifically Artificial Intelligence (AI), in revolutionizing healthcare. The author develops and utilizes a personal medical AI agent (“Haley”) and a platform (“CureWise”) to analyze complex medical data, identify patterns missed by human doctors, and research personalized treatment options. This directly connects to fostering innovation and upgrading technological capabilities within the health sector.

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

Based on the issues discussed, the following specific SDG targets can be identified:

1. Target 3.4: By 2030, reduce by one-third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being.
   • The author’s struggle with an “aggressive form of blood cancer” is a direct example of a non-communicable disease. The article highlights the importance of early diagnosis and effective, personalized treatment to prevent a lethal outcome. The failure of the standard treatment and the search for a more effective, targeted therapy (“Daratumumab plus Venetoclax”) is a clear effort to improve treatment and prevent premature mortality.
2. Target 3.8: 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 critiques the quality of healthcare services, noting the system is “overwhelmed” and “structurally incapable of giving every person the kind of thoughtful, individualized, adaptive care that they need.” The initial misdiagnosis demonstrates a failure in providing quality care, while the author’s use of AI to find a better treatment plan and collaborate with new doctors represents a move towards more effective and patient-centered care.
3. Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries… including… encouraging innovation.
   • The author’s creation of AI agents to process medical records, analyze genetic reports, and “cross-reference them with clinical literature, find case reports and studies” is a prime example of using innovative technology to enhance scientific and medical research. The AI’s ability to “connect an immunology paper from Japan, a gene-editing trial from Boston, and a retrospective analysis from Sweden” demonstrates a significant upgrade in technological capability for medical decision-making.

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

Yes, the article mentions and implies several indicators that can measure progress:

• Indicators for Target 3.4 (Non-communicable diseases):
  • Early and Accurate Diagnosis Rate: The article implies this is a key indicator. The initial failure of doctors to diagnose the cancer contrasts with the AI agent’s ability to flag a “concerning pattern” within minutes from the same data, suggesting that the speed and accuracy of diagnosis can be measured.
  • Patient Health Outcomes: A direct indicator is mentioned when the author states, “The response has been exceptional: My key cancer marker is back in the normal range.” This is a measurable clinical outcome tracking the success of the treatment.
• Indicators for Target 3.8 (Universal Health Coverage):
  • Access to Personalized Medicine: The article implies a shift from the “standard of care” for the “average patient” to individualized treatment plans based on a patient’s specific genetic markers (“the DNA fingerprint of my cancer cells”). The ability of patients to access such tailored care is an indicator of quality.
  • Patient Participation in Decision-Making: The author’s transformation from a passive patient to a “proactive participant at every level of my care” through a “partnership between human judgment and machine intelligence” is a qualitative indicator of progress towards a more collaborative and higher-quality healthcare model.
• Indicators for Target 9.5 (Innovation and Research):
  • Development of New Health Technologies: The creation of the “CureWise” app and the panel of specialized AI agents serves as a concrete indicator of innovation in the health sector.
  • Efficiency of Medical Research Synthesis: The article implies this can be measured by time and scope. The AI’s ability to synthesize vast amounts of global research “before breakfast” is a powerful indicator of enhanced research capabilities compared to human-only efforts where “No one had the time to sift through all of the research.”

4. Table of SDGs, Targets, and Indicators

Source: statnews.com