Artificial intelligence (AI) is rapidly transforming healthcare, offering innovative solutions to common challenges, from reducing clinical documentation burdens to improving healthcare data standardization. Grand View Research reports that the global AI in healthcare market size was estimated at USD 19.27 billion in 2023 and is expected to grow at a CAGR of 38.5% from 2024 to 2030.
By integrating advanced AI techniques such as machine learning (ML), natural language processing (NLP), and retrieval augmented generation (RAG), AI supports healthcare professionals—from physicians to software engineers—in making data-driven decisions, automating administrative tasks, and enhancing patient care.
However, healthcare AI implementation is not without hurdles. Clinical data is highly complex, inconsistent, and often ‘trapped’ in unstructured formats. Without robust clinical terminology, AI-driven insights risk being inaccurate and difficult to apply.
This comprehensive guide to clinical AI in healthcare explores benefits, use cases, and challenges. What are you waiting for?
Clinical AI
In the words of Catherine Zhu, Product Management Director at IMO Health, “Clinical AI, at its best, combines advanced technology, clinical terminology, and human expertise to boost healthcare data quality.”
IMO Clinical AI, infused into all our solutions, leverages:
- Robust, expertly maintained clinical terminology and coding
- Award-winning technology, including an extensive AI development platform and advanced LLMs
- A team of highly specialized experts, including NLP architects, semantic architects, clinical experts and annotators, and data scientists
By incorporating these elements, IMO Clinical AI addresses complex clinical nuances and empowers organizations to make more intelligent, data-driven decisions.
The importance of rich clinical terminology
Clinical terminology helps to translate physician language into standardized codes that support various use cases, including documentation, billing, and analytics—to name a few. Without accurate, continuously updated terminology, AI models will struggle to decode clinical concepts, leading to errors that could affect patient care, reimbursement, and downstream initiatives.
Machine learning (ML) in healthcare
ML is a specific type of AI that enables computers to learn, reach conclusions, and detect patterns with little human intervention. These models are especially adept at synthesizing large quantities of unstructured medical data ‘trapped’ inside EHRs (electronic health records). Extracting meaning and useful insights from these free-text documents—which make up 70 to 80% of all healthcare data—is challenging.
For example, clinical notes often develop gaps and inconsistencies as they are transferred between systems, making them less reliable. The manual analysis of these free-text documents is costly and resource-intensive.
Here’s one real-world example of IMO Health’s long history of applying ML in healthcare: In 2024, IMO Health partnered with a global pharma company to develop two annotated collections of medical texts—one for human papillomavirus (HPV) associated diseases and the other for pneumococcal-associated pediatric diseases (PAPD). They then leveraged machine- and deep-learning models to automate the systematic literature review (SLR) abstract screening process. The results demonstrated AI’s potential to automate abstract screenings, saving researchers valuable time and effort.
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Much like having a trusted colleague, ML works alongside physicians to automate tasks and improve documentation accuracy, enabling them to focus on what’s most important in medicine—the patients.
Some ML benefits include:
- Improving diagnostic accuracy
- Fueling better clinical decision-making
- Accelerating drug discovery and clinical trial processes
- Enhancing patient care outcomes
- Supporting research and population health reporting
Natural language processing (NLP) in healthcare
NLP is a branch of AI that enables machines to comprehend and communicate in natural language, like humans. Clinical NLP can effectively clean and standardize patient data when combined with ML, ensuring EHR records are accurate and searchable.
Some clinical NLP benefits include:
- Identifying trends in patient data to support personalized medicine and value-based care initiatives
- Supporting predictive analytics
- Optimizing billing and reimbursement
- Improving clinical decision-making
NLP applications embedded in EHRs can address nuances in clinical language, like abbreviations and negations. They can also help providers identify missed or improperly coded diagnoses, which supports the accurate capture of HCCs (hierarchical condition categories) and reduces claim denials.
To achieve scale, healthcare organizations should combine NLP-driven data normalization tools with clinical AI rooted in rich, always-current clinical terminology. This is arguably the best way to maintain data quality in healthcare, support interoperability, and deliver accurate insights.
EBOOK
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Benefits and use cases of clinical AI in healthcare
AI for task automation
AI can automate routine tasks like medical coding and clinical documentation. By reducing manual effort and increasing efficiency, AI can reduce instances of human error and maximize cost-effectiveness.
AI for medical data standardization
AI-powered data standardization improves interoperability across healthcare systems and streamlines innovation. NLP solutions in particular can automate clinical data codification, standardizing inconsistent patient data from disparate sources, such as diagnosis, procedure, medication, and lab records.
AI in clinical decision support
AI models can improve clinical decision-making by analyzing vast amounts of medical data, including patient genetics and social determinants of health (SDOH), to provide personalized care and support value-based care initiatives.
Ambient AI for clinical documentation improvement
Ambient clinical intelligence (ACI), or ambient clinical documentation can accurately convert spoken language into structured, coded, highly specific clinical data. This significantly reduces physicians’ documentation burden, streamlines clinical workflows, and enhances patient interactions.
IMO Health’s suite of LLMs and knowledge graphs are uniquely positioned to advance ambient AI with rich clinical terminology, a deep understanding of provider documentation workflows, and award-winning NLP models.
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AI in personalized medicine and predictive analytics
AI-driven predictive analytics in healthcare helps providers pinpoint individual patients at high risk for certain diseases like heart disease and diabetes. This enables doctors to take a more preventative approach to medicine, reducing overall rates of chronic diseases and healthcare costs.
AI for life sciences and drug discovery
AI has the potential to transform the life sciences industry, optimizing everything from systematic literature reviews (SLR) to clinical trials. In 2024, IMO Health researchers and Merck developed a new AI system to tackle the slow, manual process of gathering scientific data through traditional SLR methodologies.
Challenges of AI in healthcare
No one can deny that it’s an exciting time to work in the medical field but also an immensely stressful one.
Here are some of the most formidable challenges facing healthcare AI:
Data privacy and security
AI solutions must protect sensitive patient data from cyberattacks and other threats. They must also comply with industry regulations, like HIPAA. Data security is paramount at IMO Health, as demonstrated by our SOC 2 Type 2 and HIPAA certifications.
Accuracy and accountability
AI systems are only as good as the data they are trained on, meaning incomplete or inconsistent data will generate inaccurate, unusable insights. You wouldn’t cook with expired ingredients, right? As the saying goes, “garbage in, garbage out”. Organizations should partner with experienced vendors to normalize their data and integrate AI solutions thoughtfully.
Integration with existing healthcare IT systems
When adopting new technology, it’s critical to partner with a vendor that offers flexible deployment and integration options to ensure seamless integration with existing systems. IMO Health, for example, offers hassle-free integration with application programming interfaces (APIs) accessible through an intuitive developer portal.
Physician adoption and trust
Even the most promising innovations can become burdensome without proper management, triggering clinician burnout, dissatisfaction, and clinical workflow disruptions. However, organizations can implement various strategies to streamline this process and increase clinician trust.
The future of healthcare AI: Key considerations
Before implementing AI-powered solutions, healthcare professionals should ask:
1. Was this AI model trained on rich, expertly maintained clinical terminology?
2. How does this AI tool ensure data quality and standardization?
3. Will this solution integrate seamlessly into our existing EHR and tech stack?
4. What strategies can we implement to ensure clinician adoption and trust?
While AI won’t replace healthcare workers, it can enable them to deliver more personalized care, develop innovative medical treatments faster, optimize revenue, and more. The key is ensuring AI tools are built on a foundation of comprehensive medical terminology and designed to support real-world clinical workflows.
