Artificial Intelligence in Medical Coding Market By Component (Software, Services), By Technology (Machine Learning (ML), Natural Language Processing (NLP), Deep Learning, Computer Vision, Robotic Process Automation (RPA)), By Application (Inpatient Coding, Outpatient Coding, Radiology Coding, Surgical Coding, Emergency Department Coding, Pathology Coding, Insurance Claim Processing, Medical Record Auditing), By Deployment Mode (On-Premise, Cloud-Based), By Coding Type (ICD Coding, CPT Coding, HCPCS Coding, DRG Coding, SNOMED CT), By End User (Hospitals & Healthcare Systems, Ambulatory Surgical Centers (ASCs), Diagnostic Centers, Insurance Companies & Payers, Medical Coding & Billing Service Providers, Others), Global Market Size, Segmental analysis, Regional Overview, Company share analysis, Leading Company Profiles and Market Forecast, 2025 – 2035

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Industry Outlook

The Artificial Intelligence in Medical Coding Market accounted for USD 2.69 Billion in 2024 and USD 3.05 Billion in 2025 and is expected to reach USD 10.80 Billion by 2035, growing at a CAGR of around 13.47% between 2025 and 2035. AI in medical coding is the process of using machine learning and natural language processing to immediately and automatically identify what codes to use for medical diagnoses, treatments, and procedures. Due to this technology, coding errors are reduced, accuracy goes up, and billing in healthcare gets done faster. They can go through extensive patient records, select the crucial details, and use appropriate codes set by the government. AI in medical coding is becoming more popular as hospitals and clinics demand easier healthcare record-keeping and the healthcare documentation systems get more complicated and take longer to reimburse. AI is increasingly being relied upon by hospitals, clinics and insurers as they try to make operations more affordable and error-free.

Industry Experts Opinion

“Discover the essential competencies health information professionals need to thrive in collaboration with AI technologies. This presentation explores foundational skills for AI literacy that enhance efficiency, practical training frameworks, critical skill development and implementation strategies that empower professionals to confidently leverage AI tools in healthcare information settings—creating a workforce ready to maximize these powerful technologies.”

• Dr. David Marc, PhD, CHDA – Chair, Health Informatics and Information Management, The College of St. Scholastica

Report Scope:

Market Dynamics

The increasing need for faster and more accurate medical coding is encouraging the use of AI technology.

The healthcare coding profession is being compelled to implement AI because the demand for quick and accurate coding continues to rise. Healthcare processors have to deal with large amounts of data speedily to receive timely payments, satisfy payments by insurers, and adhere to government-mandated requirements. Such hand-coding of electronic claims proves to be time-consuming, causes potential errors, and could take a long time to be paid. Applications based on NLP and ML can read medical notes and easily identify the information necessary to choose appropriate codes. This means that coding is done faster and the results are more precise and standardized. With healthcare information continuously becoming more complicated and voluminous, AI is more crucial to maintain efficiency.

With quicker outcomes, AI allows for fewer coding errors to lower claim denials, and billing mistakes. Thus, healthcare providers, payers, and insurers can communicate more seamlessly. AI applications utilize newer data analysis and machine learning algorithms to keep pace with new coding guidelines and regulations like ICD-11 and CPT, primarily automatically. Thus, healthcare organizations can become more compliant and avoid financial penalties.

The use of AI in this domain makes expert coders focus more on complicated cases, while AI takes care of easy coding. However, hospitals, insurance companies, and billing agencies now look toward AI solutions to maximize their revenue cycle management. With more demanding regulations and greater numbers of patients comes greater responsibility in medical coding, and individuals within the sector will need quicker, more efficient coding, rendering AI a primary instrument assisting this sector to grow.

A shortage of skilled medical coders is pushing healthcare providers to rely on AI-based solutions.

Having enough skilled medical coders can be difficult, AI technology is becoming an important rising factor in the medical coding market. As healthcare develops and patient data keeps growing, more accurate and timely coding is now necessary. Still, there is a growing difference between how many experienced coders exist and the amount of coding that needs to be done. Since there is a shortage, healthcare providers have begun using AI to quickly go through, understand and code the records of many patients. Through the use of natural language processing and machine learning, these tools pull out important information from medical documents, set the correct codes and ease the work of limited human staff in this area. Besides filling the labour shortage, using machines makes company operations more accurate and efficient.

AI is viewed by healthcare organizations as an answer to continuity problems, especially where things must happen fast. Because of a loss of experts in coding and fewer people entering the field, hospitals and clinics are relying on AI to ensure their activities remain legal and their billing is correct without any decreases in productivity. Because of AI, effective administration is possible for healthcare organizations, so they are less likely to require delays in billing patients or to hire outsiders for coding services.

The scarcity of programmers is prompting clinics of all types and especially midsize practices used to coding by hand, to increase their use of AI. Because AI does not require as many hard-to-find experts, healthcare businesses can now look ahead to scaling and sustaining their work. This therefore means that the shortage of coders is making companies realize the importance of digital transformation and encouraging more investment in AI for medical coding.

High costs of installing and maintaining AI systems make it hard for smaller healthcare providers to adopt them.

High costs related to setting up and maintaining AI are a key factor that slows down the expansion of Artificial Intelligence (AI) in the Medical Coding market. For small healthcare firms and clinics, the sum needed to purchase advanced AI systems is often very large. Not only must the AI software be bought, but also upgrades to hardware, its integration with EHR systems, worker training, and continued support are needed. Since many small- and medium-sized healthcare facilities are short on funds, it is tough for them to make these IT solutions a priority. For these reasons, the companies still depend on manual coding, which takes more time and may not be accurate. As a result, the introduction of artificial intelligence has created a divide between big hospitals and smaller practices.

Staying up to date with AI through needed updates and maintenance costs extra money for the business. All changes in medical coding guidelines, ICD codes, and billing requirements should be reflected in the updates to AI-powered systems. It’s common for these updates to depend on software companies’ services and add to your cost. The additional costs of cybersecurity, data privacy rules, and fixing problems put off many smaller providers from considering it. Many times, these systems aren’t managed by IT teams, making the process more difficult. Consequently, the cost needed for deploying AI is preventing many organizations in the medical industry from getting the most out of automation. As long as AI is expensive and hard to implement, it will be difficult to use AI for medical coding on a large scale.

Advances in natural language processing (NLP) are helping AI tools better understand and code medical terms.

The growth of Artificial Intelligence in the Medical Coding market is being driven by recent progress in NLP. AI systems use NLP to analyze, understand and interpret large files of medical information, which includes doctor’s notes, clinical summaries, discharge reports and pathology records. In earlier times such information was hard to analyze because of the many inconsistencies in language, abbreviations and context-bound terms. Due to the progress in NLP, AI can now analyze medical records well and deliver correct codes for them. Because of this, coding errors decrease, and it becomes easier and more certain for healthcare professionals to use the information.

NLP also helps make it possible to use healthcare professionals’ words in coding and billing documents. If context and semantics are understood by NLP-based systems, they can respond to different medical specialities, regional language variants, and changing terms in medicine. Because of this, AI can be adopted in more places and medical settings. With progress in NLP, AI will be able to code more difficult tasks on its own with very little need for human help. It makes operations more efficient while reducing the paperwork that healthcare employees must handle. Because of this, progress in NLP helps AI become more useful and acceptable in medical coding, making it easier for healthcare systems to use.

The rise of telehealth services is increasing the need for automated and efficient coding solutions.

Telehealth’s quick rise has offered a great possibility for growth in the Artificial Intelligence in the Medical Coding market. Virtual consultations have become popular, leading to a big increase in remote healthcare activities. Because of this increase, medical coders must now ensure both their speed and accuracy. Manual coding is often not fast enough to handle the large and fast amount of work in telehealth. Besides, AI-powered solutions support healthcare professionals in tackling these digital files by doing the job in less time, more consistently and without adding to their administrative duties.

AI technologies using NLP can analyze information in a patient’s audio recordings, video chats, and records created during telehealth appointments. With these tools, correct diagnostic and procedural codes can be found and used right away, reducing mistakes and helping get payments sent out rapidly. With telehealth rising in areas where access to medical care is limited, the need for this kind of smart technology will grow. Such systems use AI to support a variety of telehealth tools, which allows them to be used in many healthcare settings. Because we rely more on digital healthcare, it is important to use smart coding tools, where AI can help improve the telemedicine infrastructure. Because of this, telehealth is changing how health care is provided, as well as providing new chances for AI to grow in medical coding.

Segment Analysis

Based on Component, the Artificial Intelligence in Medical Coding Market is segmented into Software and Services. One of them, the software sector, is dominating today. The primary reason for their dominance is that sophisticated AI-based coding tools now manage challenging coding issues, require less human input, and cause bills to be more accurate. Large healthcare operations prefer these applications more because they can be joined to EHRs and RCM tools easily, which are employed across healthcare. Upgrades and revisions to the software's algorithms ensure it stays abreast of present regulations and medical vocabulary. With patients expecting fewer mistakes in healthcare, the number of providers who are using the best software platforms is increasing, thereby making the market for those platforms grow.

Based on Technology, the Artificial Intelligence in Medical Coding Market is segmented into Machine Learning (ML), Natural Language Processing (NLP), Deep Learning, Computer Vision, and Robotic Process Automation (RPA). Natural Language Processing (NLP) is top of the line. NLP enables one to make sense of disorganized clinical notes, resulting in faster and more accurate coding. It facilitates the automatic conversion of doctor paperwork into regular codes that minimize errors and maximize the likelihood of claims being accepted. These technologies are useful in processing compound language in medical reports and ensuring that information is correctly interpreted. As clinical data expands, the relevance of NLP is likely to improve, thereby giving the field a stronger lead.Bottom of Form

Regional Analysis

The North American Artificial Intelligence in Medical Coding market is a leading one due to its advanced healthcare infrastructure and high adoption of digital health technologies. In the United States businesses are mandating AI coding solutions to reduce human errors, verify billing speed and accuracy, and accelerate claims processing. AI is utilized by most health providers to improve the process of handling their finances. Innovations from industry companies, innovative new solutions, and transparent regulation are driving the industry forward. In addition, because lots of healthcare data exist within the region, leveraging it enables the AI models to gain credibility. The deployment speed is being accelerated by partnerships between healthcare organizations and tech companies. Because finding and recruiting programmers takes more time and because salaries are increasing, companies are embracing automation solutions. In general, North America is progressive and entrepreneurial in terms of AI in medical coding

The Asia-Pacific region is emerging as a promising market for Artificial Intelligence in Medical Coding, driven by growing investments in healthcare digitization and expanding hospital networks.
India, China, Japan, and South Korea are increasingly observing how AI can assist them in administering more effectively and reducing coding errors in healthcare. With increasingly sophisticated health systems within this region gathering more patient information, it is imperative now to implement precise automated coding. Various AI companies are entering the marketplace to help businesses with local and international requirements. While rural communities continue to grapple with digital technologies, generally speaking, the movement toward enhancing healthcare is robust. Due to the efforts of the government and the private sector, digitalization is taking flight in Southeast Asia.

Competitive Landscape

A mix of well-known healthcare IT companies and startups focused on AI is found in this market, all aiming to improve how quickly and correctly medical codes are applied. Examples of firms providing AI-based coding for healthcare are 3M Health Information Systems, Optum (UnitedHealth Group) and Cerner Corporation, whose products are used by many hospital staff. Due to years of experience and access to massive amounts of data, these companies can tighten and maintain precise coding standards. Many are noticing startups such as Fathom, Savista and Ciox Health since they are producing AI tools created for use in specific areas of healthcare. A major feature of this competition is new ideas, safe data and easy connection to existing Electronic Health Records systems. Companies are enhancing their R&D budgets and teaming up with healthcare groups to design AI systems that can be used on a wide scale.

Artificial Intelligence in Medical Coding Market, Company Shares Analysis, 2024

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Recent Developments:

• In November 2024, Maverick Medical AI released Maverick CodePilot™ to the public. This tool has automated real-time coding with regulations, so it can suggest text for your record, making your documentation and revenue better.
• In August 2024, CodeMetrix revealed that its CodaMetrix AI Platform earned Epic Toolbox designation for the self-driving coding category. 20 leading health systems in the U.S. use the platform to change patient information into complete medical codes.

Report Coverage:

By Component

• Software
• Services

By Technology

• Machine Learning (ML)
• Natural Language Processing (NLP)
• Deep Learning
• Computer Vision
• Robotic Process Automation (RPA)

By Application

• Inpatient Coding
• Outpatient Coding
• Radiology Coding
• Surgical Coding
• Emergency Department Coding
• Pathology Coding
• Insurance Claim Processing
• Medical Record Auditing

By Deployment Mode

• On-Premise
• Cloud-Based

By Coding Type

• ICD Coding
• CPT Coding
• HCPCS Coding
• DRG Coding
• SNOMED CT

By End User

• Hospitals & Healthcare Systems
• Ambulatory Surgical Centers (ASCs)
• Diagnostic Centers
• Insurance Companies & Payers
• Medical Coding & Billing Service Providers
• Others

By Region

North America

• U.S.
• Canada

Europe

• U.K.
• France
• Germany
• Italy
• Spain
• Rest of Europe

Asia Pacific

• China
• Japan
• India
• Australia
• South Korea
• Singapore
• Rest of Asia Pacific

Latin America

• Brazil
• Argentina
• Mexico
• Rest of Latin America

Middle East & Africa

• GCC Countries
• South Africa
• Rest of the Middle East & Africa

List of Companies:

• 3M Health Information Systems
• Nuance Communications
• Aidéo Technologies
• Fathom
• CodaMetrix
• Buddi AI
• Diagnoss
• Clinion
• Semantic Health
• Arintra
• aiHealth
• AGS Health
• Episource
• Datavant
• CitiusTech

Frequently Asked Questions (FAQs)