AI in Healthcare Software: Use cases already in production

Artificial Intelligence (AI) is rapidly becoming a core technology in modern healthcare. According to Grand View Research (2026), the global AI in healthcare market was valued at USD 36.7 billion in 2025 and is expected to reach USD 505.6 billion by 2033, growing at a CAGR of 38.9%. At the same time, 79% of healthcare organizations are already using AI technologies.  

This growth is driven by the increasing need for greater efficiency, higher diagnostic accuracy, and better patient outcomes. AI supports a wide range of healthcare applications, from medical imaging analysis and predictive analytics to personalized treatment planning and drug discovery.   

But how is AI being used in the real-world healthcare sector? In this article, we'll explore the most common AI use cases already in production and break down how each application delivers value for healthcare providers and patients.  

Key takeaways  

• AI is becoming a core technology in healthcare, with 79% of healthcare organizations already adopting it.   

• AI in healthcare spans multiple technologies, with machine learning holding the largest market share at over 35% in 2025.  

• The most common AI use cases in healthcare span four areas: clinical documentation and workflows, medical imaging and diagnostics, patient treatment and care, and data analytics. 

•  System integration remains the biggest barrier to scaling healthcare AI. In Singapore, AI integration is further complicated by the NEHR architecture, in which applications must connect through MOH-certified HIMS rather than via a public API. 

AI in Healthcare software   

What is AI in Healthcare?   

AI in healthcare refers to the use of computers and machine-based systems to simulate human intelligence and perform complex tasks that support healthcare delivery. Rather than replacing healthcare professionals, AI helps them work more efficiently by processing large volumes of data, identifying patterns, and generating insights.  

Common forms of AI used in healthcare

• Machine learning (ML): Training algorithms using health data to create models capable of performing tasks. The machine learning segment held the largest share (over 35.0%) in 2025 (Grand View Research, 2026).   

• Natural language processing (NLP): Using machine learning to enable computers to understand and communicate with human language, including clinical notes, medical records, and research publications.  

• Deep learning: An advanced branch of machine learning that uses multi-layer neural networks to analyze complex data such as medical images, genomic data, and physiological signals. Deep learning methods can be used to automate tasks that typically require human intelligence   

• Generative AI (Gen AI): Using Large Language Models (LLMs) to create original outputs, such as clinical documentation, patient summaries, medical reports, and research insights.  

• Robotic process automation (RPA): Using AI in computer programs to automate administrative and clinical workflows.  

Real-world use cases of AI in Healthcare   

01. AI in Clinical documentation  and workflows    

The core problems   

Clinical documentation remains one of the biggest contributors to clinician workload.  

• Excessive documentation burden due to manual and repetitive administrative tasks (data entry, note-taking, EHR updates…).   

• Inconsistent and fragmented clinical data makes it hard for integration. For example, in Singapore, standardized documentation is essential for seamless integration with the National Electronic Health Record (NEHR.  

• Inefficient clinical workflows, as manual processes slow care coordination, delay decision-making, and reduce overall operational efficiency.   

The AI approach    

Ambient AI scribe automatically captures conversations between clinicians and patients, transcribes them in real time, and generates structured clinical notes such as SOAP notes by using speech recognition, NLP, and Gen AI. Clinicians only need to review and approve the draft before it is saved to the EHR. 

AI-assisted documentation improves the quality and consistency of clinical records by suggesting standardized terminology, identifying missing information, and automatically structuring documentation based on clinical context.  

AI summarization and semantic search analyze data from multiple clinical systems to generate concise patient summaries and retrieve relevant information using natural language queries. Clinicians can quickly access patient data, enabling faster and more informed decisions. 

AI-powered workflow automation streamlines repetitive tasks, such as generating referral letters, discharge summaries, medical coding suggestions, and follow-up documentation.  

Technical stack   

• Speech Recognition: Converts clinician–patient conversations into text 

• Natural Language Processing (NLP): Understands medical terminology, extracts key clinical information, and identifies relevant entities such as diagnoses, medications, and symptoms. 

• Large Language Models (LLMs): Generate structured clinical notes, patient summaries, referral letters, and other medical documents from clinical conversations. 

• Machine Learning (ML): Continuously improves documentation accuracy, coding suggestions, and workflow automation by learning from historical clinical data. 

• Semantic Search & Retrieval (RAG): Retrieves relevant patient records, laboratory results, and previous notes to provide context-aware responses and summaries. 

• FHIR / HL7 APIs: Enable secure, standardized data exchange across healthcare platforms. Check our practical guide to EMR/EHR integration patterns. 

• Cloud infrastructure & Security: Supports scalable AI deployment while ensuring compliance with healthcare regulations such as PDPA in Singapore. 

A notable example of AI in clinical documentation comes from Singapore, where SingHealth has implemented Note Buddy, a Microsoft-powered AI documentation solution, to transform clinical documentation and enhance quality of doctor-patient interactions. By 2025, the tool has supported over 5,000 healthcare staff, generating more than 67,000 medical and administrative notes.  

02. AI in Medical imaging and diagnostics   

The core problems 

The increasing imaging volumes and workforce shortages have placed significant pressure on radiologists and healthcare systems. 

• Growing imaging volumes due to rising demand for CT, MRI, X-ray, ultrasound, and mammography. 

• Time-consuming image interpretation, particularly for complex or multi-image studies. 

• Diagnostic variability and human error can lead to missed findings or inconsistent interpretations. 

• Delayed diagnosis and reporting, affecting treatment decisions and patient outcomes. 

In Singapore, the shortage of radiologists remains a significant challenge. The country has only 7.6 radiologists per 100,000 population, lower than the European average of 13 and the UK's 8.5, highlighting the pressure on medical imaging services (PMC, 2023). 

The AI approach 

AI software for medical diagnosis is increasingly being adopted to help radiologists and clinicians analyze medical images, prioritize urgent cases, and support faster, more accurate clinical decision-making. 

• AI automatically analyzes medical images to detect abnormalities such as tumors, fractures, lung nodules, or signs of stroke. AI highlights suspicious regions and provides quantitative measurements, helping radiologists improve diagnostic accuracy while reducing interpretation time. 

• AI reviews imaging studies immediately after acquisition and prioritizes urgent cases based on the likelihood of critical findings.  

• AI combines imaging findings with clinical information, laboratory results, and patient history to suggest potential diagnoses, assess disease severity, and support clinical decision-making. The final diagnosis remains under the clinician's supervision. 

• AI automatically generates structured radiology reports by extracting key findings from medical images and organizing them into standardized report templates.  

Technical stack  

• Computer Vision (CV): Detects, segments, and classifies abnormalities in medical images. 

• Deep Learning: Learns complex imaging patterns for disease detection and image interpretation. 

• Large Language Models (LLMs): Generate structured radiology reports and summarize imaging findings. 

• Machine Learning (ML): Improves diagnostic accuracy by learning from large annotated imaging datasets. 

• Image Segmentation Models: Identify organs, lesions, tumors, and anatomical structures for quantitative analysis. 

03. AI in Patient treatment and care   

The core problems  

Patient treatment and care involve complex, continuous decision-making and coordination across clinicians, patients, and healthcare systems. 

• Lack of personalized treatment due to differences in medical history, conditions, medications, and treatment responses. 

• Limited visibility into patient conditions between clinical visits. 

• Complex treatment monitoring, including tracking symptoms, vital signs, medication adherence, and treatment progress.  

• Inefficient care coordination leads to missed follow-ups, delayed interventions, and gaps in patient care. 

The AI approach  

Many healthcare organizations partner with specialized AI chatbot companies to develop secure, compliant, and healthcare-specific conversational solutions. 

AI chatbots and virtual health assistants interact with users (patients, clinicians, and admin) through conversational interfaces. 

• 24/7 patient support: Answers health-related questions. 

• Personalized health guidance: Provides context-aware information based on the patient's symptoms, history, or care plan. 

• Medication reminders: Reminds patients to take medications and follow prescribed treatment schedules. 

• Appointment management: Helps patients book, reschedule, or cancel appointments. 

• Clinical escalation: Flags high-risk symptoms or situations and routes patients to healthcare professionals. 

According to Singapore General Hospital (2025), an AI chatbot that helps doctors comprehensively assess a patient’s health before an operation will save Singapore General Hospital up to 660 hours of doctors’ time, equivalent to $200,000 in costs annually. 

Technical stack  

• LLMs (Large Language Models): Understand patient queries and generate natural, context-aware responses. 

• NLP & Speech Recognition: Processes text or voice input, identifies symptoms, medical terms, and patient intent. 

• RAG (Retrieval-Augmented Generation): Retrieves relevant information from trusted medical knowledge bases, patient records, or care guidelines before generating responses. 

• Patient Data & EHR Integration: Connects the assistant with patient history, medications, appointments, and care plans to provide personalized support. 

• FHIR / HL7 APIs: Enables standardized and secure data exchange between the AI assistant and EHR/EMR or other healthcare systems. 

04. AI in Data Analytics   

The core problems 

Healthcare organizations generate large volumes of data from EHRs, medical devices, laboratory systems, claims, and patient interactions. However, turning this data into actionable insights remains challenging. 

• Fragmented healthcare data, as patient and operational data are spread across multiple systems and formats. 

• Time-consuming data analysis due to manual data preparation and analysis.  

• Limited predictive capabilities, while healthcare organizations need to predict risks and future outcomes. 

• Big data makes it difficult to turn data into actionable insights  

The AI approach 

There are four common types of analysis, including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.  

• Descriptive analytics - What happened: Uses AI to analyze historical healthcare data and summarize what happened, such as patient volumes, hospital utilization, treatment outcomes, or operational performance. 

• Diagnostic analytics - Why it happened: Uses AI to identify patterns, correlations, and anomalies to explain why something happened.  

• Predictive analytics - What will happen: Uses machine learning to analyze historical and real-time data to predict what is likely to happen, such as patient deterioration, readmission risk, disease progression, or future demand for healthcare resources. 

• Prescriptive analytics - What should be done: Goes one step further by using AI to recommend what should be done, such as prioritizing high-risk patients, suggesting interventions, optimizing resource allocation, or recommending operational actions. 

Technical stack  

• Machine Learning (ML): Identifies patterns and generates predictive insights from healthcare data. 

• NLP & LLMs: Extracts insights from unstructured clinical data and enables natural-language data queries. 

• Data Lake / Data Warehouse: Centralizes healthcare data from multiple sources for analysis. 

• FHIR / HL7 APIs: Enables standardized data exchange between healthcare systems. 

• BI & Data Visualization: Turns AI-generated insights into dashboards, reports, and actionable insights. 

How to develop AI software for healthcare  

There are two common approaches for implementing AI software for the healthcare sector. Whether to build in-house or partner with a specialized healthcare AI software vendor depends on multiple factors. While in-house development gives healthcare organizations greater control but requires significant internal AI expertise, resources, and development time, outsourcing to a specialized vendor provides access to experienced AI and healthcare teams, faster deployment, and lower internal development burden. 

Choosing an AI development partner goes beyond technical skills. You need a team that understands your healthcare needs, can work with your existing systems, and can support the solution as it scales. 

Key barriers holding AI back  

Despite the significant value AI can bring to healthcare organizations, effective implementation can be challenging. 

Integration with legacy systems 

AI does not operate in isolation; it needs to connect with existing healthcare systems such as EHRs, EMRs, laboratory systems, and hospital management platforms. However, many healthcare organizations still rely on legacy systems built on different technologies and architectures, making AI integration more complex.  

The challenge is not simply connecting AI to existing software, but integrating it seamlessly without disrupting the systems and workflows healthcare organizations already rely on. McKinsey (2026) reported that integration challenges ranked as the first barrier to scaling Gen AI.  

In Singapore, AI integration is further complicated by the National Electronic Health Record (NEHR) architecture. There is no open public API that allows AI applications to connect directly to the NEHR. Instead, only MOH-certified Health Information Management Systems (HIMS) can exchange data with the NEHR. What it means to AI solutions vendors:  

• Identify the HIMS used by the healthcare provider 

• Integrate with that system 

• Rely on the APIs and capabilities it exposes 

Regulatory & compliance requirements 

AI in healthcare operates within a highly regulated environment, where patient safety and data privacy are top priorities. Before an AI solution can be deployed, organizations must ensure it complies with relevant regulations, industry standards, and clinical requirements.  

For example, in Singapore, several strict regulations can influence how AI is implemented:  

• The Personal Data Protection Act (PDPA) governs how organizations collect, use, disclose, and protect patients' personal data, requiring AI solution vendors to implement robust data privacy, security, and governance measures 

• MOH AI in Healthcare Guidelines (AIHGle) aims to ensure patient safety and enhance trust in the use of AI in healthcare. AIHGle provides a consolidated set of recommendations and good practices for healthcare AI developers.  

Clinician trust & adoption 

Even the most advanced AI solutions can fail to deliver value if clinicians do not trust or adopt them in their daily practice. Since AI increasingly supports clinical documentation, diagnosis, and treatment decisions, healthcare professionals need confidence that AI outputs are accurate, reliable, explainable, and clinically appropriate.  

Singapore has also identified trust as a cornerstone of healthcare AI adoption. In the Artificial Intelligence in Healthcare Guidelines (AIHGle 2.0), the Ministry of Health states its vision to "shape a future where innovation and trust go hand in hand".  

Conclusion 

Healthcare software development in Singapore is evolving rapidly, driven by digital transformation, strong regulatory support, and the growing adoption of AI. From clinical documentation and diagnostics to patient care and data analytics, AI is enabling healthcare organizations to improve operational efficiency, clinical outcomes, and patient experiences. 

Beyond healthcare, AI is also reshaping industries such as travel, finance, and logistics, making AI no longer a future trend but a strategic investment for organizations looking to stay competitive in the digital era. 

Adamo APAC - Build smarter healthcare software, with AI at the core   

Adamo APAC  - the Singapore office of Adamo Software is founded to help businesses accelerate digital transformation by combining Singapore-led consulting with Vietnam's engineering expertise. Adamo APAC delivers custom software development, AI and data services, dedicated development teams and staff augmentation across Asia-Pacific. We specialize in the Healthcare sector - the vertical most technology companies treat as a sideline.  

At Adamo APAC, we develop healthcare software with a strong focus on data security, regulatory compliance, system interoperability, and clinical reliability. Beyond building AI-powered features, we help healthcare organizations implement AI responsibly by addressing key challenges such as model transparency, bias testing, data lineage, and human oversight. Our AI development practices are aligned with Singapore's IMDA Model AI Governance Framework and the Model AI Governance Framework for Agentic AI, enabling organizations to adopt AI in a secure, compliant, and trustworthy way. 

Partnering with Adamo APAC to develop AI solutions continues to be the prevalent strategy. 

FAQs   

01. We do not have a lot of data. Can AI still help us? 

For some use cases (especially LLM-based approaches), large datasets aren't required. For others (custom classifiers, computer vision), data volume is critical. During discovery, we assess your data situation and recommend approaches that match your data reality. 

02. How does Adamo APAC handle data privacy when training AI models?  

We follow PDPA-aligned practices including data minimization (using only necessary data), anonymization techniques where appropriate, secure development environments accessed only via VPN, and contractual safeguards for cross-border data transfer. 

03. How much does an AI project cost? 

AI project costs vary widely based on complexity, data readiness, and production requirements. Proof-of-concept engagements typically range from USD 30,000-80,000. Full AI development projects with production deployment range from USD 100,000-500,000. Pricing depends heavily on the data work required. Projects with clean, labeled data are significantly faster and lower-cost than projects requiring extensive data preparation. We provide detailed estimates after the discovery phase.