Artificial Intelligence in Health Diagnostics: Role in Triage and Public Health

Artificial Intelligence in health diagnostics is changing how healthcare systems assess, identify, and manage diseases across both individual and population levels. From streamlining patient triage to enhancing public health surveillance, AI offers the potential for faster, more accurate, and cost-effective diagnostic solutions. This transformation is not just a future possibility, it's already reshaping the present landscape of medicine. ^[1]

The global AI diagnostics market was valued at US $1.16 billion in 2023, projected to reach US $5.44 billion by 2030 (CAGR = 24.6%).

AI in medical triage has brought remarkable efficiency to emergency care and primary consultations. AI-powered symptom checkers can assess symptoms, prioritize patient needs, and suggest appropriate care pathways. These systems reduce the burden on frontline healthcare workers while ensuring that critical cases are attended to with urgency. For example, machine learning in healthcare can analyze patient histories, real-time vitals, and symptom descriptions to support automated medical decision-making during peak hours in hospitals. ^[2]

One of the most impactful applications of healthcare diagnostics using AI lies in improving diagnostic accuracy. AI models trained on massive datasets can detect patterns in medical imaging—such as CT scans, MRIs, and X-rays-often with higher precision than the human eye. This technology assists radiologists, pathologists, and clinicians in early disease detection using AI, enabling timely intervention and improved patient outcomes. Algorithms developed for cancer screening, cardiovascular conditions, and neurological disorders exemplify the power of artificial intelligence in medicine.

Beyond individual diagnostics, AI in public health is transforming how governments and organizations respond to health challenges. AI tools for public health surveillance can process data from diverse sources, social media, hospital records, mobile apps, to identify outbreaks and assess population health trends. For instance, during the COVID-19 pandemic, AI models helped predict infection hotspots, optimize resource allocation, and evaluate the effectiveness of containment strategies. ^[3]

Several global health systems have already integrated artificial intelligence in health diagnostics to enhance care delivery:

• Babylon Health (UK): Utilizes AI-powered symptom checkers to support patient triage via mobile applications.

• Google DeepMind: Developed an AI model capable of detecting over 50 eye conditions from retinal scans.

• India's Aarogya Setu App: Used AI to track COVID-19 exposure and inform public health planning.

• WHO’s EIOS System: Employs AI for early detection of potential public health emergencies globally.

Aidoc’s AI algorithm reviewed over 14,000 CT scans across multiple centers, achieving 84.8% sensitivity and 99.1% specificity for incidental pulmonary embolism detection.

UK data on 45,000 adults (average age 55) over 10 years found AI analysis of retinal scans identified 29 vascular features predictive of stroke, matching traditional risk models.

In Australia, an AI model based on lung ultrasound achieved 96.6% accuracy in diagnosing pneumonia and COVID‑19.

These real time examples demonstrate how AI tools not only enhance diagnostic precision but also offer scalable solutions for public health surveillance.

As AI becomes more integrated into healthcare systems, the ethical use of data, algorithmic transparency, and equitable access will be vital. The future of diagnostic accuracy with AI algorithms depends not just on technological advancement but also on collaborative policymaking and robust data governance.

Artificial intelligence in health diagnostics stands at the intersection of innovation and necessity. From AI in medical triage to public health surveillance, its transformative impact is evident across all tiers of healthcare. As we embrace the future, ensuring responsible deployment and continuous research will be essential to fully realize the potential of AI-driven health systems.

References:

1. Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again.

2. Liu, X., Faes, L., Kale, A. U., et al. (2019). "A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis." The Lancet Digital Health, 1(6), e271-e297.

3. Bullock, J., Luccioni, A., Pham, K. H., et al. (2020). "Mapping the landscape of artificial intelligence applications against COVID-19." Journal of Artificial Intelligence Research, 69, 807–845.

4. Aidoc. “Aidoc.” Accessed June 13, 2025. https://www.aidoc.com/.

5. Charles Darwin University, United International University, and Australian Catholic University. “Researchers Train AI to Diagnose Lung Diseases.” Charles Darwin University News, January 24, 2025. https://www.cdu.edu.au/news/researchers-train-ai-diagnose-lung-diseases.

(Input from various sources)

(Rehash/Sakshi Thakar/MSM)