The Widening Gap Between Afghanistan’s Health System and the World in the Age of Artificial Intellig
Part I: Afghanistan’s Health System — Fragmented Islands in a Sea Without Data
Afghanistan’s health system is not a unified structure but a scattered network of public and private facilities operating independently, without coordination or oversight. This fragmented model not only deviates from global standards but struggles even with basic health governance.
🔹 Structural Challenges
Lack of Data Registration and Tracking Systems There is no national health database to record clinical events, patient histories, or disease trends. Most facilities rely on manual or isolated records, with no capacity for analysis or data sharing.
Isolated Operations of Health Facilities Each clinic, doctor, and paraclinical unit makes decisions, executes procedures, and evaluates outcomes independently. No centralized authority or harmonized system exists.
Absence of Alert and Trend Analysis Mechanisms Even when non-communicable diseases like diabetes or hypertension rise significantly in a region, no analytical system investigates causes or triggers preventive action.
Lack of Unified Treatment and Management Standards Therapeutic protocols, referral systems, and ethical frameworks are not standardized. Each facility operates based on its own resources and philosophy.
Normalization of Suboptimal Diagnosis and Treatment Due to economic hardship and cultural limitations, both patients and providers have adapted to minimalistic care models, reinforcing systemic stagnation.
🔹 Lagging Behind Even the Non-AI World
While many countries have implemented electronic health records (EHRs), disease registries, and trend analysis systems—even without AI—Afghanistan remains in a pre-digital phase.
Countries like India, Pakistan, and Iran have adopted national digital health platforms.
Organizations such as WHO and UNICEF have launched data-driven health initiatives in similar contexts.
In Afghanistan, even basic systems like DHIS2 and HMIS are incomplete, limited, and disconnected from private sector facilities.
🔹 Consequences of This Reality
ConsequenceDescription
Inability to manage health crisesIncluding COVID-19, tuberculosis, and chronic diseases
Lack of evidence-based policymakingThe Ministry of Public Health cannot plan based on real data
Exclusion from global health collaborationsAbsence of data prevents participation in international projects
Increased costs and reduced effectivenessRepetitive, inaccurate treatments and poor referral practices
In this context, the global rise of artificial intelligence in healthcare threatens to exponentially widen the existing gap.
🧩 Part II: Artificial Intelligence and the Redefinition of Global Health Systems
Over the past decade, AI has evolved from an emerging technology into a foundational pillar of global health management. Countries are redesigning their health infrastructures to leverage AI’s capabilities in analysis, prediction, and personalization—not just in hospitals and clinics, but across policy, education, and pharmaceutical innovation.
🔹 Key Applications of AI in Global Healthcare
DomainAI Application
Disease diagnosisMedical image analysis (X-ray, MRI), pattern recognition via machine learning
Personalized treatmentProtocol design based on genetic data, medical history, and lifestyle
Patient monitoringReal-time tracking via sensors and algorithms at home or in clinics
Drug discoveryMolecular simulation, clinical data analysis, and accelerated R&D
Resource managementForecasting drug needs, optimizing equipment allocation, reducing costs
Mental healthTherapeutic chatbots, sentiment analysis, and AI-assisted counseling
🔹 Microsoft Platforms Empowering Digital Health
Microsoft offers a robust ecosystem of cloud and AI tools to support global health transformation:
Microsoft Azure Health Data Services Secure storage, management, and analysis of health data using FHIR and HL7 standards 🔗 Azure Health Data Services
Microsoft Fabric Unified environment for integrating health data, analyzing it with Power BI, and building predictive models 🔗 Microsoft Fabric for Healthcare
Azure AI Services Tools for computer vision, natural language processing, and speech synthesis for medical applications 🔗 Azure AI Overview
FHIR Database Integration Enables standardized patient data sharing across healthcare facilities
🔹 A Near Future: Data-Driven, Algorithmic Medicine
Soon, every patient will have a digital health profile containing genetic data, medical history, lifestyle patterns, and treatment responses. These profiles will be stored and analyzed by AI platforms to:
Design individualized treatment protocols
Enable coordinated, data-driven decision-making across facilities
Transform static medical references like Harrison and Merck Manual into dynamic, adaptive models
🧩 Part III: Afghanistan’s Position in the Global Health Landscape
While the world embraces AI-driven health systems, Afghanistan faces multilayered challenges—technological, structural, economic, and cultural—that hinder its ability to keep pace.
🔹 Structural and Managerial Barriers
No National Health Insurance System Most Afghans pay out-of-pocket for care, preventing systematic data collection and analysis.
Lack of Referral and Coordination Mechanisms Public and private facilities operate in silos, with no data exchange or regional trend analysis.
Shortage of Skilled Workforce in Health Technology Doctors, administrators, and technicians lack training in digital health and AI, leading to resistance and superficial use of digital tools.
🔹 Economic and Technological Constraints
Weak Connectivity and Database Infrastructure Many facilities lack stable internet access. Local databases are incomplete, and global standards like FHIR and HL7 remain unimplemented.
Limited Access to Cloud Platforms and AI Tools Platforms like Azure, Fabric, or Google Health are rarely used. Even when available, lack of training and infrastructure hinders adoption.
High Costs of Technology Deployment With constrained health budgets, AI implementation requires international support, strategic policy, and prioritization.
🔹 Afghanistan at the Edge of Global Algorithms
According to a 2025 article in the European Journal of Medical Research, countries lacking representative health data are gradually excluded from global AI models. This means:
Diagnostic algorithms are not optimized for Afghan populations
Personalized treatments are designed using foreign datasets
Afghanistan is sidelined in international digital health initiatives
A 2024 study in IJMRA also found that AI chatbots could support mental health in Afghanistan, but lack of local data and ethical frameworks blocks their development.
🧩 Part IV: Strategic Recommendations for AI-Driven Health Integration in Afghanistan
Despite the widening gap, Afghanistan can take meaningful steps to align with global health transformation—through localized planning, international collaboration, and strategic use of proven technologies.
🔹 1. Build a National Health Data Infrastructure
Develop a unified health database using FHIR and HL7 standards
Use Microsoft Azure Health Data Services for secure storage and analysis
Implement EHR systems in public and private facilities with cloud integration
🔗 Microsoft Cloud for Healthcare – Overview
🔹 2. Train Health Professionals in AI and Digital Health
Offer courses like Azure AI Fundamentals (AI-900) for doctors, managers, and technicians
Translate and localize Microsoft Learn resources for team use
Establish academic units in medical universities focused on digital health and AI
🔹 3. Connect Health Facilities via Analytical Platforms
Use Microsoft Fabric to unify scattered data and enable predictive analytics
Design management dashboards with Power BI to monitor diseases, resources, and regional trends
Deploy Dragon Ambient Experience (DAX) for automated clinical documentation at point of care
🔗 Healthcare Data Solutions in Microsoft Fabric
🔹 4. Develop Ethical and Legal Frameworks for AI Use
Collaborate with WHO and UNDP to draft responsible AI regulations
Define principles like transparency, fairness, and patient privacy
Establish oversight bodies to evaluate AI models and algorithms in clinical settings
🔹 5. Promote a Culture of Technology Adoption in Healthcare
Produce educational content, analytical articles, and public webinars on AI in medicine
Encourage facilities to use digital tools for diagnosis, treatment, and patient management
Support health-tech startups focused on AI-driven solutions
🔚 Final Reflection
In a world where healthcare is being redefined by data, algorithms, and personalization, Afghanistan must not remain on the sidelines. Despite structural and economic challenges, opportunities exist to bridge the gap—especially through platforms like Azure and Fabric, and by training a new generation of health professionals. The time to act is now—before this gap becomes an unbridgeable divide.
در اینجا، مسائل زیستمحیطی و چالشهای صحی افغانستان با نگاهی علمی و تحلیلی بررسی میشوند—تا سلامت انسان و طبیعت در مرکز توجه قرار گیرد، و راهحلهایی مبتنی بر دانش و تجربه پیشنهاد شود