AI technologies in healthcare: What US companies need to know 

Break down the technologies behind AI in healthcare software development, helping US-based companies integrate it effectively.

Healthcare leaders these days have shifted from whether and where AI is relevant to how AI can be built responsibly and at scale. McKinsey (2026) reported that around 50% of their organizations surveyed have deployed the first use case of AI more than six months ago, reflecting their open vision and technological readiness in leveraging these technologies.  

In this article, we will break down the tech stack behind Artificial Intelligence (AI), learning how it can be used, how it works, and tips for building AI responsibly in healthcare in the US.  

Key insights  

• US healthcare firms have made significant investments in healthcare AI solutions, with focus on core technologies such as Machine Learning, Natural Language Processing and Cloud-based tools.  
• In the US, around 85% of healthcare leaders are implementing Generative AI (Gen AI) with many projects running from Proof-of-Concept (PoC) to deployment. 
• AI can not replace and compensate for the shortage of many key allied health occupations.  It can only play a supporting tool, while humans still play the primary role. 
• In the US, when developing AI software in the healthcare sector, companies need to strictly follow regulations especially HIPAA and FDA.  
• The hard part of building AI responsibly is the data quality as a better AI model cannot compensate for bad data. Around 63% of organizations do not have or are unsure whether they have the right data management practices for AI. 

AI in Healthcare software application overview  

What is it?  

AI in healthcare software application is the use of AI technologies in healthcare software to improve patient care, support clinical decision-making, and streamline healthcare operations, 

AI can be integrated into existing systems of healthcare firms, such as Electronic Medical Record (EMR) or Electronic Health Record (EHR). Also, AI can be built into new applications, such as virtual health assistants or patient monitoring platforms.  

Market overview and key trends  

The US AI in healthcare market size is expected to increase from USD 18.1 billion in 2025 to USD 222.9 billion by 2033. Many US-based health firms have made huge investments in healthcare AI solutions. The advancements in Machine Learning (ML), Natural Language Processing (NLP), and Cloud-based AI tools have fostered the innovation of AI in the healthcare sector. (Grand View Research, 2026).  

Key trends shaping the AI in healthcare market in the US:  

• The increasing demand for personalized and remote healthcare solutions.  
• A significant 85% of healthcare leaders, including health systems, payers, and technology groups, are implementing Generative AI in healthcare, with many projects running from Proof-of-Concept (PoC) to deployment.  
• The widespread adoption of telehealth solutions for remote patient monitoring is fostering the use of AI in telehealth.  
• Regulations, particularly HIPAA, have set standards for protecting patient data privacy and security, which helps reduce the risk of data breaches.  

Why AI matters in healthtech 

The potential of AI in healthtech is big. AI plays important role in healthcare software development nowadays. It can do more than just automate predefined tasks or handle complex information. So what is the reason why AI matters in healthtech in the US:  

It lies in the high demand for healthcare services in the US and the increasing requirement for expertise and personalization. Moreover, the healthcare system in the US is complex and includes many complex administrative procedures. When handling a large number of patients, AI can automate administrative tasks, reducing time spent on paperwork and focusing on patient care. Also, thanks to modern technologies which we will break down later, AI can personalize patient care experience at scale, improving their satisfaction.  

Another notable point is that by 2038, the US will face a shortage in many key allied health occupations, such as 43,490 dispensing opticians or 12,770 respiratory therapists (HRSA, 2025). However, it does not mean that AI can replace and compensate for healthcare personnel. In healthcare, AI can only play a supporting tool, while humans still play the primary role. AI can alleviate this shortage pressure, especially in administrative tasks.  

Common use cases of AI in Healthcare  

AI applications in healthcare span a wide range of areas. Here is exactly how AI can be used in healthcare.  

AI Health Assistant 

AI Health Assistant has changed the way healthcare organizations deliver care for patients. It differs from traditional chatbots, which basically just answer questions based on predefined rules, and analyzes data to perform tasks.  

AI workflow automation  

AI can be used in healthcare to run repetitive work, such as scheduling, documentation, coding, billing, and supporting the operational decisions. In other words, by automating manual tasks, AI can optimize workflow within US-based healthcare firms and improve overall operations. For deeper insights into what AI workflow automation in healthcare really covers, check our article.  

AI integration in EHR/ EMR  

AI can be integrated into the existing platforms of healthcare companies, particularly EHR and EMR. It analyzes patient data and summarizes medical documentation, supporting doctors in making more informed decisions.  

AI personalization in medicine 

Powered by Machine Learning (ML) and Natural Language Processing (NLP), AI analyzes data, including medical history, treatment responses, and other generic information, to tailor treatment plan to each individual.  

AI predictive analysis  

ONC (2024) reported that 71% of hospitals reported using predictive AI integrated with the EHR, up from 66% in 2023, affirming that AI is increasingly used to analyze patient data and predict potential health risks.  

AI in Healthcare software application: Technical breakdown  

Technologies used  

Machine Learning (ML) segment dominated the market with the largest revenue share in 2025, according to Grand View Research. Machine Learning allows computers to learn patterns from historical data to predict outcomes or classify information without manually programming every rule. It is widely used in disease prediction, medical imaging analysis, patient risk assessment, and treatment recommendation systems  

Deep Learning (DL) is a branch of ML that uses multi-layer neural networks to learn complex features from data, such as images, sounds, and biological signals. It can: 

• Analyze X-ray, CT, and MRI images 
• Detect abnormal areas in medical images  
• Analyze signals from health monitoring devices  

Natural Language Processing (NLP) supports processing, analyzing, and extracting meaning from human language, especially medical documentation with specialized terms.  

Generative AI (Gen AI) can generate content in multiple formats (text, sound, or image), while Large Language Models (LLMs) are a group of models trained on big data to process and generate content. 

Retrieval-Augmented Generation (RAG) combines LLMs with a mechanism for searching information from multiple sources. The system retrieves relevant information depending on the users’ requests.  

Deployment model  

Cloud: Deploying software on a cloud computing infrastructure, such as AWS, Microsoft Azure, or the Google Cloud. The vendor manages most of the physical infrastructure, while the organization uses and manages the services as needed.  

Best suited when:  

• You need to deploy AI application quickly without investing heavily in servers. 
• User volume or data processing demands change frequently.  
• You want to leverage AI services, data storage, and analysis available in the cloud. 

On-premises: Deploying servers and infrastructure owned or directly managed by the organization, typically located on the premises or the private data centre environment of the organization.  

Best suited when:  

• You need direct control over infrastructure, access rights, and data flow. 
• Security policies or internal requirements restrict sending data to external services. 
• You have the infrastructure team to operate and maintain the system.  

Hybrid combines on-premises infrastructure with the cloud, allowing organizations to choose the appropriate environment for each application, data type, or processing tasks. 

Best suited when: 

• You want to keep data on-premises but still need to use cloud services.  
• You use a legacy healthcare system and want to add AI without migrating the whole system to the cloud.  
• You need a balance between data control, flexibility, and processing power.  

AI governance in healthcare in the US 

As the US takes healthcare seriously, implementing AI software applications in healthcare needs to be done very carefully. Companies have to pay attention to specific regulations, ensuring compliance to build AI responsibly.  

Here are some important regulations or frameworks for building AI responsibly in the US market:  

Healthcare Insurance Portability and Accountability Act (HIPAA)  

HIPAA is a US federal law aimed at protecting patient health information. For AI healthcare software, HIPAA specifically relates to how systems collect, use, store, and secure protected health data.  

• The HIPAA Privacy Rule establishes national standards to protect medical records and other identifiable information.  
• The Security rule establishes national standards to protect electronic protected health information that is created, received, used, or maintained by its business associate.  
• Breach Notification Rule requires business associates to provide notification following a breach of unsecured protected health information.  

Food and Drug Administration (FDA)  

The FDA does not regulate AI; it regulates medical devices, including AI-enabled medical devices. Here are several AI-enabled medical devices:  

• An imaging system that uses algorithms to give diagnostic information for skin cancer in patients 
• Software that uses deep learning to sharpen images 
• AI systems that can detect diabetic retinopathy from retinal images 
• AI algorithms to automate insulin dosing based on continuous glucose monitor (CGM) readings 

The FDA reviews medical devices through a premarket pathway. Also, it reviews and clears modifications to medical devices.

Challenges and how to address 

AI can do a lot of things, but that does not mean AI is omnipotent. There are many challenges when developing AI software applications for the healthcare sector.  

Data security  

Healthcare firms handle a large amount of patient data. When AI is introduced, this data needs to be processed across systems or shared with third-party AI providers. This can lead to higher risk of data breaches, unauthorized access, or violations of regulations such as HIPAA. That’s why protecting data needs to be a priority from the early stages of AI development.  

HHS reported 663 notifications of breaches of unsecured PHI affecting 500 or more individuals that occurred during 2024. These reported breaches affected around 242,908,056 individuals.  

Data engineering for AI  

Data engineering is the foundational backbone of every AI system. The hard part of building AI responsibly is data quality, as a better AI model cannot compensate for bad data. The AI system can only perform well when the input data is well-processed. According to Gartner (2025), around 63% of organizations do not have, or are unsure whether they have, the right data management practices for AI.  

As such, before developing AI solutions in healthcare, businesses need to evaluate their data first. What data do you have? Where do you store it? Is it structured, reliable, or fit for the use case?  

Integration with legacy software  

Many healthcare organizations in the US still rely on legacy systems to manage EHRs, patient data, billing, and daily operations. Integrating AI into these systems can be challenging because old software may not support modern APIs or standard data formats, making it difficult to exchange information smoothly.  

Healthcare data may also be stored across systems, requiring additional work to connect and standardize it before AI can use it effectively. Without proper integration, AI tools may operate in isolation, creating extra work for healthcare staff instead of improving efficiency.

Where Adamo Software fits  

Adamo Software is a trusted AI development company based in Vietnam, delivering reliable solutions for clients across the globe. We build custom AI, machine learning, generative AI, and multimodal systems for production. Our AI development process helps your business at every stage, starting from problem identification, data collection and preparation, then AI-based model development and model evaluation before integrating with existing systems and monitoring.  

Moreover, Adamo Software focuses on Healthcare, offering all-in-one digital health tech to transform patient care and operations.  

Enhance healthcare delivery with tailored healthcare software solutions with Adamo Software.