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Abstract

The integration of artificial intelligence (AI) in healthcare has accelerated dramatically between 2023 and 2025, fundamentally transforming disease diagnosis and pharmaceutical dispensing practices. This review examines the current state and emerging trends of AI-driven healthcare technologies, focusing on diagnostic applications and automated medicine dispensing systems. Recent data indicates that physician adoption of healthcare AI increased by 78% from 2023 to 2024, with two-thirds of physicians now utilizing AI tools in their practice. The convergence of machine learning algorithms, computer vision, and robotics has created unprecedented opportunities for precision medicine, automated pharmacy operations, and enhanced patient outcomes. This paper analyses the technological advancements, clinical applications, market dynamics, and future implications of AI in healthcare delivery systems.

Keywords

Artificial Intelligence, Healthcare Automation, Disease Diagnosis, Pharmacy Automation, Medicine Dispensing, Digital Health

Introduction

The healthcare landscape has witnessed a paradigm shift with the rapid adoption of artificial intelligence technologies between 2023 and 2025. This transformation encompasses two critical domains: AI-enhanced disease diagnosis and automated medicine dispensing systems. The global AI in healthcare market is experiencing unprecedented growth, with projections indicating an annual growth rate of 37.3% through 2030. This exponential expansion reflects the technology's maturation and its proven capability to address longstanding challenges in healthcare delivery. The period from 2023 to 2025 has been particularly significant, marking a transition from experimental AI applications to mainstream clinical adoption. Healthcare institutions worldwide have increasingly integrated AI-driven solutions into their diagnostic workflows and pharmaceutical operations, driven by the need for improved accuracy, efficiency, and patient safety. This review synthesizes current developments, analyses implementation patterns, and projects future trajectories in AI-driven healthcare [1-5].

2. AI in Disease Diagnosis: Current State and Advancements (2023-2025)

2.1 Diagnostic Accuracy and Clinical Integration [6-8]

The period 2023-2025 has witnessed remarkable improvements in AI diagnostic capabilities across multiple medical specialties. Machine learning algorithms now demonstrate superior performance in detecting various conditions, including infectious diseases, cancer, and cardiovascular disorders. AI systems have shown particular strength in medical imaging interpretation, pathology analysis, and pattern recognition in complex diagnostic scenarios. Recent clinical studies have demonstrated that AI can match or exceed human diagnostic accuracy in specific domains while significantly reducing diagnostic time. The technology's ability to process vast amounts of patient data, including electronic health records, laboratory results, and imaging studies, enables comprehensive diagnostic assessments that consider multiple variables simultaneously.

2.2 Physician Adoption and Workflow Integration [9-12]

Healthcare provider adoption of AI diagnostic tools has accelerated substantially. Survey data from 2024 reveals that 66% of physicians are now using healthcare AI, representing a 78% increase from 2023 levels. This rapid adoption reflects growing confidence in AI capabilities and the technology's proven ability to enhance clinical decision-making without replacing physician expertise. AI applications in diagnostic medicine currently focus on several key areas: documentation of billing codes, creation of medical charts and visit notes, development of discharge instructions and care plans, and clinical decision support. These applications have demonstrated measurable improvements in workflow efficiency and diagnostic consistency.

2.3 Emerging Technologies and Future Applications [13-15]

The diagnostic AI landscape continues to evolve with the introduction of advanced algorithms capable of multi-modal data integration. Recent developments include AI systems that can analyze patient symptoms, laboratory data, and imaging results simultaneously to provide comprehensive diagnostic recommendations. These systems are particularly valuable in complex cases where multiple differential diagnoses must be considered. Predictive analytics represents another frontier in AI diagnostics, with systems now capable of identifying patients at risk for specific conditions before symptoms manifest. This proactive approach enables early intervention and preventive care strategies that can significantly improve patient outcomes.

Table No. 1. AI in Disease Diagnosis: Developments and Clinical Integration (2023–2025)

Section

Focus Area

Key Findings

Implications

Diagnostic Accuracy and Clinical Integration

- AI performance in disease detection
- Application in medical imaging, pathology, and EHRs

- AI matches or exceeds human diagnostic accuracy in specific areas (e.g., oncology, cardiology)
- Rapid interpretation of complex diagnostic data
- Integration of multi-source clinical data (EHRs, lab results, imaging)

- Enhances diagnostic accuracy and speed
- Supports data-driven clinical decisions
- Reduces diagnostic errors and time delays

Physician Adoption and Workflow Integration

- Physician usage trends
- Clinical documentation and decision support

- 66% physician adoption in 2024 (78% ↑ from 2023)
- Applications: automated charting, care plans, billing, CDS tools

- Boosts workflow efficiency
- Maintains physician control while enhancing decisions
- Reduces clerical burden and improves documentation quality

Emerging Technologies and Future Applications

- Multi-modal diagnostic systems
- Predictive analytics for early detection

- Systems integrate imaging, symptoms, labs for real-time diagnosis
- Predictive AI identifies risks before symptoms appear

- Enables early diagnosis and preventive strategies
- Critical for complex, multi-disease risk profiles
- Future potential in personalized and precision medicine

3. AI-Driven Medicine Dispensing and Pharmacy Automation

3.1 Market Growth and Technology Adoption [16, 17]

The pharmacy automation market has experienced substantial growth during 2023-2025, with global market projections indicating a compound annual growth rate (CAGR) of 10.12% through 2034. The automated dispensing machines market specifically is expected to reach $6.22 billion by 2029, driven by technological advancements and an aging population requiring more complex medication management. This growth reflects the healthcare industry's recognition of automation's potential to address critical challenges including medication errors, operational efficiency, and pharmacist workload management. AI-powered systems are increasingly integrated into pharmacy operations, providing sophisticated capabilities for prescription processing, inventory management, and patient safety monitoring.

3.2 Smart Pharmacy Technologies [18-24]

The concept of "smart pharmacies" has emerged as a central theme in pharmacy automation during this period. These facilities integrate multiple AI technologies including robotics, computer vision, and predictive analytics to create comprehensive automated dispensing systems. Key innovations include:

Robotic Dispensing Systems: Advanced robotic systems now handle medication selection, packaging, and dispensing with unprecedented accuracy and speed. These systems can process prescriptions significantly faster than traditional methods while reducing human error rates.

Fig. 1: Automated medication dispensing system with computer by the Initiation of Artificial Intelligence

Source: https://healthmanagement.org/products/view/automated-medication-dispensing-system-with-computer-kl60-kirbylester

AI-Powered Inventory Management: Machine learning algorithms optimize medication inventory by predicting demand patterns, identifying slow-moving stock, and automating reorder processes. This capability is particularly valuable in managing specialty medications and reducing waste.

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Arnab Roy
Corresponding author

Assistant Professor of Pharmacology, Department of Pharmacy, Faculty of Medical Science and Research, Sai Nath University, Ranchi, Jharkhand 835219, India.

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Eliška Nováková
Co-author

Pharmacist, Adam’s Pharmacy, 8, Wenceslas Square 775, New Town, 110 00 Prague, Czechia.

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Lejla Hadžic
Co-author

Lecturer, Faculty of Pharmacy, University of Tuzla, Dr. Tihomila Markovi?a 1, Tuzla Grad 75000, Bosnia and Herzegovina.

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Lars Janssens
Co-author

Lecturer, Dept. of Pharmacy, Yerevan State University, 1 Alek Manukyan St, Yerevan 0025, Armenia.

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Aaron Dogba Yassah
Co-author

Research Scholar, Kalinga University, Kotni, Atal Nagar-Nava Raipur, Chhattisgarh 492101, India.

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Faith Ruth Dixon
Co-author

Research Scholar, Kalinga University, Kotni, Atal Nagar-Nava Raipur, Chhattisgarh 492101, India.

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Clerick Clemento Conneh
Co-author

Student, Bachelor of Computer Application, Kalinga University, Kotni, Atal Nagar-Nava Raipur, Chhattisgarh 492101, India.

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Souradeep Sarkar
Co-author

Assistant Professor, Sai College of Pharmacy, Barbigha, Bihar 811101, India

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Subham Kumar Lohani
Co-author

Student, Department of Pharmacy, Sai Nath University, Ranchi, Jharkhand 835219, India.

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Ayush Kumar
Co-author

Student, Department of Pharmacy, Sai Nath University, Ranchi, Jharkhand 835219, India.

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Sajid Ansari
Co-author

Student, Department of Pharmacy, Sai Nath University, Ranchi, Jharkhand 835219, India.

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Alok Kumar
Co-author

Student, Department of Pharmacy, Sai Nath University, Ranchi, Jharkhand 835219, India.

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Ankit Kumar Srivastava
Co-author

Student, Department of Pharmacy, Sai Nath University, Ranchi, Jharkhand 835219, India.

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Madhu Vishwakarma
Co-author

Student, Department of Pharmacy, Sai Nath University, Ranchi, Jharkhand 835219, India.

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Kajal Kumari
Co-author

Student, Department of Pharmacy, Sai Nath University, Ranchi, Jharkhand 835219, India.

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Priojit Ghosh
Co-author

Assistant Professor, DmbH Institute of Medical Science, Dadpur, West Bengal 712305, India.

Eliška Nováková, Lejla Hadži?, Lars Janssens, Aaron Dogba Yassah, Faith Ruth Dixon, Clerick Clemento Conneh, Priojit Ghosh, Subham Kumar Lohani, Ayush Kumar, Sajid Ansari, Alok Kumar, Ankit Kumar Srivastava, Madhu Vishwakarma, Kajal Kumari, Arnab Roy*, Souradeep Sarkar, AI-Driven Disease Diagnosis and Medicine Dispensing: A New Era in Healthcare, Int. J. Sci. R. Tech., 2025, 2 (6), 296-305. https://doi.org/10.5281/zenodo.15612466

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