AI application in healthcare: A practical guide for Singapore businesses

The AI in healthcare market in Singapore is expected to reach revenue of US $1,655 million by 2023, with the software solution segment accounting for the largest share (Grand View Research, 2025). AI has transformed how healthcare firms in Singapore work. MOH predicted that AI Agents are emerging beyond basic AI Assistants, while Generative AI is moving into everyday clinical workflows. The National Helthtech agency in Singapore, Synapxe, has developed Tandem, which is a common and secure Gen AI platform across public healthcare system.  

Instead of measure AI adoption by the number of tools we deploy, it is more important to ask: What can doctors, nurses, and care teams can do better with the support of AI? This guide will break down how AI is applied in healthtech in healthcare, providing readers with practical insights into this technology.  

Key takeaways 

• While AI Agents are predicted to emerge beyond AI Assistants, Gen AI is moving into everyday clinical workflow.  

• Instead of focusing solely on the number of AI tools deployed, it is necessary to ask, "What can clinicians do better because AI is there?"  

• AI is applied in a wide range of aspects in the healthcare sector. IT leaders have trained AI solutions to explore how they can drive efficiency, with 50% reporting that either piloting or actively using AI in their business, while another 45% still explore adoptions. 

• Healthcare firms can combine cloud-based and on-premises solutions to deploy their AI solutions as this balances data control with flexibility and leverages resources. 

What is AI in healthcare software?  

AI in healthcare software is the integration of AI technologies such as Machine Learning, Natural Language Processing, Generative AI, or Computer Vision, into healthcare software to analyze data, support decision-making, and automate tasks.  

How AI works in healthcare software  

AI can be integrated into many healthcare systems such as Electronic Health Records (EHRs), Hospital Management Platforms, or Remote Patient Monitoring (RPM) to support multiple tasks.  

The typical AI workflow includes:  

• Collect healthcare data  

• Prepare and process data  

• Apply AI models  

• Generate insights or recommendations  

• Review, integrate, and improve  

AI healthcare software vs. Traditional healthcare software  

Traditional healthcare software primarily operates based on pre-programmed rules, focusing on record-keeping, data management, and process execution  

AI healthcare software integrates AI to analyze complex data, identify patterns, predict health risks, and automate tasks such as documentation or supporting clinical decision-making.  

Key AI technologies  

• Generative AI (GenAI) generates new content, such as text, images, or audio, based on learned data.  

• Large Language Models (LLMs) are AI models trained on a large amount of language data, capable of understanding and generating text in natural language.  

• Machine Learning (ML) allows computers to learn from data to identify patterns and make predictions or decisions without specific programming.  

• Deep Learning (DL) uses multi-layer neural networks to learn and process complex data patterns.  

• Natural Language Processing (NLP) helps computers analyze, process, and understand human natural language.  

• Computer Vision analyzes and interprets information from images and videos.  

Common applications of AI in healthcare 

AI is applied in a wide range of aspects in the healthcare sector. Many leaders in the IT industry have trained AI solutions to explore how they can drive efficiency, with 50% reporting that either piloting or actively using AI in their business, while another 45% still explore adoptions. There are several AI use cases already in production in the healthcare sector in Singapore that you should not ignore, covering the problem, AI approaches, and suggested tech stack.  

• AI in clinical documentation and workflow  

• AI in medical imaging and diagnostics  

• AI in patient treatment and care 

• AI in data analytics  

Also, an AI Healthcare Assistant is one of the most common AI applications in healthcare, going beyond traditional chatbots that simply answer questions. It can search, retrieve information, summarize documentation, and report.  

More notably, AI workflow automation in healthcare is the next big thing. It is considered a real game changer for healthcare in Singapore, supporting streamlined workflows and increasing productivity.  

Benefits of AI in healthcare  

Cost saving  

AI helps reduce operational costs by automating repetitive tasks, minimizing errors in record processing, and optimizing the use of healthcare manpower, time, and resources.  

Hyper-personalization  

AI analyzes health data, including medical history and individual status to support personalized treatment, care, and health monitoring plans.  

Increased patient satisfaction 

AI improves the patient experience through 24/7 support, quicker responses, appointment reminders, and personalized information.  

Support clinical decision-making  

Thanks to its ability to analyze clinical data, test results, and medical images to provide information, identify risks, and suggest treatment options, AI can help doctors make more informed decisions.  

Streamline clinical workflow  

AI can automate tasks such as medical record keeping, summarizing patient files, scheduling appointments, and handling paperwork, reducing administrative workload and allowing clinicians to focus more on patient care.  

How to build an AI application in healthcare  

AI integration  

AI integration into existing systems in healthcare organizations in Singapore is a real challenge for many businesses due to the complexity of legacy software. Here are several ways of AI integration:  

 

These four AI integration approaches are non-exclusive, meaning that one healthcare firm can use a combination of methods, depending on their security requirement, control level, and current infrastructure.  

AI deployment  

After deciding on how to integrate AI into healthcare systems, you need to make decision on how to deploy this technology. There are three common ways for AI deployment.  

Can we combine both approaches? Yes, we have the hybrid approach. Healthcare firms can combine cloud-based and on-premises to power AI components. This method balances data control with flexibility and leverages resources from both environments. However, this AI deployment approach requires complex architecture and managing data synchronization, security, and connectivity.  

Training vs. Customizing an AI model  

When developing AI software, healthcare firms in Singapore need to choose how to build or customize a model that fits their data, goals, and budget. Not all AI applications require training a new model from scratch. You can consider the following four approaches:  

Training from scratch  

Training from scratch is building and training an AI model from scratch using its own data, allowing high control over the architecture and training process. However, it requires large amounts of data, significant computing resources, a technical team, and high development costs. 

Best fit for: Healthcare firms with large-scale, specialized medical datasets, specific technical requirements, and huge resources to develop, evaluate, and maintain AI models long-term.  

Fine-tune a pretrained model  

Fine-tuning is the process of training a pre-trained AI model using specific data to improve its performance for a specific task. This approach is more efficient than training from scratch, but still requires high-quality data and technical expertise. 

Best fit for: Healthcare applications need AI models that adapt better to medical terms, reporting formats, or specific tasks (classifying clinical documents or extracting information).  

Pretrained model  

Pretrained models are AI models that have been trained on large datasets and can be used directly without further training with specific data. This method helps shorten deployment time and reduce initial development costs, but may not fully meet the specific requirements of the healthcare firms. 

Best fit for: Projects requiring rapid deployment of common AI functionalities. 

RAG  

RAG is a method that combines large language models (LLMs) with mechanisms for retrieving information from external data sources to provide context before generating responses. This method allows AI to use updated documentation without retraining the model when data changes. However, its effectiveness depends on the quality of the documentation, its accessibility, and the access control mechanisms. 

Best fit for: Healthcare applications that need to generate answers based on specific information sources. 

Steps to build  

Step 01. Identify the healthcare needs and define AI use cases  

Identify the healthcare challenges and choose the right AI use cases. Not all healthcare problems can be solved with an AI application. With more than 8 years of experience working with client across global, Adamo APAC realizes that around 20% of our AI discovery engagements end with a recommendation.

Step 02. Assess data availability and quality  

Evaluate available healthcare data for completeness, accuracy, and relevance. Ensure data privacy, access permissions, and compliance with applicable regulations. 

Step 03. Choose the right AI approach and technology stack  

Select suitable AI technologies based on the use case, such as machine learning, computer vision, or generative AI. Choose the appropriate models, frameworks, and infrastructure. 

Step 04. Design the architecture and integration  

Plan how the AI application connects with existing healthcare systems, databases, and APIs. Include security controls and reliable data exchange mechanisms. 

Step 05. Develop and customize the AI model  

Build the AI solution using an existing model, fine-tuning, custom training, or RAG, depending on the requirements. Validate its outputs using relevant healthcare data. 

Step 06. Test the application for accuracy, safety, and compliance  

Test the application's functionality, AI accuracy, security, and reliability. Validate clinical outputs and ensure compliance with applicable healthcare regulations. 

Step 07. Deploy the application and train end users  

Deploy the application in a suitable environment, starting with a pilot if necessary. Train healthcare staff to use the system effectively and handle potential errors. 

Step 08. Monitor performance and improve the application  

Track KPIs, model accuracy, user feedback, and security issues after launch. Regularly update the model and application to maintain performance, safety, and reliability. 

Key considerations of AI implementation  

Data quality and interoperability  

AI applications rely on data quality to produce reliable outputs. Healthcare data needs to be accurate, complete, and consistent. However, patient information is often scattered across systems and stored in different formats, making it difficult to prepare data for AI development. Healthcare organisations should establish data validation processes and use interoperability standards such as HL7 FHIR or HL7 v2 to facilitate data exchange between systems. 

AI healthcare compliance  

Building AI solutions in Singapore has to meet strict regulations in this country. AI compliance in healthcare is a challenge for many software development companies, even specialized ones. Singapore takes healthcare seriously, and for good reasons. Many healthcare leaders are concerned about the capabilities of data breaches when integrating AI As such, having technical practices to control is necessary.  

AI integration with existing healthcare systems  

Integrating AI with existing electronic health records (EHRs), hospital management systems, and clinical workflows can be challenging, particularly when organisations rely on legacy software. Poor integration may lead to duplicated data, workflow disruptions, or additional work for healthcare professionals.  

Businesses should assess existing infrastructure, define integration requirements early, and use APIs or healthcare data standards where appropriate to ensure smooth data exchange. 

Human-in-the-loop 

AI should support healthcare professionals rather than replace human judgement, particularly in clinical decision-making and other high-risk tasks. A human-in-the-loop approach allows qualified professionals to review AI-generated recommendations, verify outputs, and intervene when the system produces inaccurate or uncertain results.  

Defining clear review and escalation procedures helps improve patient safety, maintain accountability, and build trust in AI applications. 

Final thoughts  

Singapore aims to become a trusted AI hub in the Asia Pacific region. Therefore, building AI responsibly has become the top priority in this country. Building AI and integrating it seamlessly is challenging, but ensuring data security is harder. The development of AI in healthcare does not mean that it can replace doctors, nurses, or care teams. AI just plays a supporting role, enhancing patient care experience and optimizing clinical workflow.  

Deploy AI solutions in the healthcare sector with Adamo APAC 

Adamo APAC supports mid-market and enterprise companies in moving beyond AI experimentation. Our approach is shaped by what we have learnt from the projects that we worked on. Our AI application development starts with problem breakdown to define whether AI is the right approach and the scope. Then we build the solution and validate before going to the production and optimization process.  

Also, we have built healthcare software with PDPA, HIPAA, and regional compliance built into the architecture. What Adamo APAC offers:  

• Telehealth & Telemedicine 

• EHR/EMR system & Health Information Exchange  

• Remote Patient Monitoring (RPM)  

• Hospital & Clinic Management  

• Mobile Health Solutions (mHealth)  

• Healthcare Data Analytics  

Building healthcare software that needs to be compliant, secure, and reliable? Talk to us.