AI in Healthcare: 8 Technologies and Applications

What can healthcare AI do and how is it used?

Sangsun Moon
AI in Healthcare: 8 Technologies and Applications

As the use of AI in major industries grows, so does its application in healthcare. With global big tech companies like Google and Microsoft showing results in healthcare AI, the field is moving from research to real-world applications. In this article, we'll explore the need for healthcare AI, its applications, enabling technologies, and its advantages and limitations.

Healthcare AI, why we need it

Why is artificial intelligence important for healthcare? There are many reasons for this, but the big categories are first, accuracy, and second, objectivity.

1. accuracy of disease diagnosis

IBM Watson's announcement of an AI doctor was met with concern by many physicians, and it's still a contentious area. But in some areas, AI has already been able to diagnose diseases as accurately as doctors, and those areas are expanding. 

Modern artificial intelligence can find highly complex patterns in high-dimensional data, such as video, better than humans. Object Detection and Image Segmentation technology has made tremendous progress in recent years, and given the quality of the training data and sufficient training volume, AI can be more accurate than humans at reading images to diagnose cancer, identify tumors, etc.

2. objectify and integrate diagnostics

Have you ever had to travel from one major hospital to another in search of an accurate diagnosis and the right surgical specialist for a major surgery? In this case, you can take your imaging data on a CD from hospital A to hospital B, but in reality, you'll most likely have to reimage and diagnose again.

Since each hospital has different functions and specifications of medical equipment, and each hospital has its own diagnostic know-how, there were various constraints to integrating this knowledge into one objective knowledge system, such as conflicts of interest and inefficiencies in the integration process.

In AI, on the other hand, the integration of this know-how is very intuitive and easy. For example, if we bring together 10 top-notch doctors, have them display their diagnoses for a given set of data, and then train them into a single model, we can easily incorporate the collective intelligence of those 10 doctors into the AI. In this sense, AI is also a very efficient knowledge sharing system.

Applications of Healthcare AI

Medical image analysis

Medical image analysis with AI

AI can detect tumors, cancers, diseases, and abnormalities with high accuracy and speed through the analysis of various medical images such as MRI, CT, X-ray, and ultrasound. Due to the aforementioned accuracy and objectification, the reliability of the analysis results is constantly increasing. In fact, when compared to human readings, AI is outperforming in its ability to detect anomalies, which can help with early detection and accurate diagnosis.

Quick diagnostics and personalized prevention

With a wide range of disease and treatment data, medical AI can quickly analyze medical images, patient records, and symptoms and suggest appropriate treatments. This saves time for doctors and medical staff and helps provide reliable care for patients.

This enables healthcare AI to utilize an individual's health data and algorithms to provide personalized prevention plans. Personalized health guidance and disease prevention strategies can be created based on an individual's genes, vital signs, lifestyle data, and more.

AI can also be used in conjunction with patient data to build prognostic models. For example, genetic data or data from a patient's medical records and vital signs can be used to predict the prognosis of cancer and create a personalized treatment plan.

Insights from healthcare big data

Humans have limited physical time to experience and learn. But AI has no such physical limitations. AI can learn from an astronomical number of Analyze medical data and synthesize it to diagnose and predict diseases with new insights. This enables healthcare providers to identify idiosyncrasies in patient populations, outbreaks of infectious diseases, and more, and intervene early.

It also supports medical research, such as clinical trial results and drug side effects and effectiveness. AI can be useful in many areas of medical research, such as developing new treatments or expanding medical knowledge through the analysis of medical data.

For the reasons mentioned above, medical AI has great potential for improving the accuracy of diagnosis and treatment, faster diagnosis and prevention, and analyzing medical data. With the development and proper utilization of these technologies, better outcomes can be achieved in the field of medicine.

Technologies and examples of healthcare AI applications

Computer  vision (CV, computer vision)

AI in Radiology
AI in Radiology: Pros & Cons, Applications, and 4 Examples

Computer vision technology is used to analyze medical imaging data to detect and quantify tumors, abnormalities, and diseases.

For example, in breast cancer screening, computer vision technology analyzes breast images to identify the size, shape, and location of tumors. This enables doctors to accurately detect and diagnose them, providing an opportunity for early treatment.

Natural Language Processing (NLP)

Natural language processing applied to healthcare AI

Natural Language Processing (NLP) technology is utilized to assist in the processing of medical records and the interpretation of medical information. This can be used to analyze Analyze medical documents and use them for decision making.

With natural language processing technology, AI can read and interpret medical records to derive patterns and key information about diseases. Doctors can quickly and effectively process a variety of patient cases and minutes, and more accurately formulate appropriate treatment plans and prevention strategies.

Machine learning

Machine learning learns and analyzes patterns and relevant information in medical data to support decision-making. It is used in many areas of healthcare, including predicting prognoses, classifying diseases, and monitoring patients.

For example, it links patient data with prognostic information to predict the occurrence and progression of certain diseases. This allows doctors to create personalized treatment plans and improve the quality of care for their patients.

👉 What is F1 Score, an accuracy measure for machine learning?

Deep learning

Deep learning is a major technology utilized in medical image analysis and pattern recognition. It supports decision-making through a network structure of artificial neural networks that learn from a variety of medical image data.

Deep learning technology uses neural network models to analyze medical images. It has been shown to excel in a variety of medical applications, such as diagnosing cancer, detecting anomalies, and predicting patient outcomes.


Robotics technology is applied in healthcare to surgical robots and automated medical systems. Robotics is used to perform medical procedures and treat patients through precise manipulation and automated systems.

For example, surgical robotic systems can complement a surgeon's maneuvers to perform precise and safe surgeries. 로보틱스는 소형 camera and manipulation tools to deliver precise surgical and rehabilitation sessions, improving patients' chances of recovery.

These technologies are driving advances in medical AI in many areas of healthcare, helping to provide more accurate and personalized care for doctors and patients.

Healthcare AI Applications - International

Medical image analysis


DeepMind's medical image analysis AI
Diagram showing how CoDoC is learned, where the existing predictive AI model remains unchanged. (Source: DeepMind)

Alphabet, the owner of Google, has an AI research company called DeepMind. In healthcare, DeepMind is developing AI solutions for medical image analysis, providing the ability to automatically interpret medical images to detect diseases and tumors.

Using raw pixel data as input, DeepMind developed general purpose artificial intelligence (AGI) techniques and perfected structures that learn from experience. The goal of DeepMind is to automatically interpret medical image data using machine learning and deep learning algorithms, detect diseases and tumors, and improve diagnostic accuracy.

DeepMind's medical image analysis technology has shown great results in practice. For example, by applying deep learning algorithms to breast cancer-related medical images, DeepMind was able to improve the accuracy of tumor detection. DeepMind is expanding their medical AI solutions globally through international collaborations and research.

When Alphabet's AI solutions improve the accuracy and efficiency of medical image analysis, healthcare providers can more accurately detect diseases and abnormalities in patients and develop appropriate treatment plans. This helps improve patient outcomes and treatment effectiveness in healthcare settings.


Arterys combines humans and AI to improve patient outcomes through previously unattainable precision medicine and insights. (Source: Arterys)

Arterys is a developer of a medical imaging AI platform that uses artificial intelligence for medical image analysis and diagnosis. Arterys provides a cloud-based platform to analyze medical imaging Analyze your data in real time and deliver accurate results and decision support.

Arterys' lung cancer AI solution provides the ability to analyze medical images in real-time to quantify the size, shape, and density of lung masses and nodules to help diagnose and track lung cancer. This enables doctors to get accurate and consistent lung cancer analysis results and make informed decisions about their patients' treatment plans.

Arterys' achievements demonstrate the potential of AI in healthcare, helping to increase accuracy and efficiency in lung cancer analysis. Arterys is constantly working to advance medical imaging AI technology and its application in the field.

Manage medical records


AWS HealthScribe
Introduction to AWS HealthScribe - Automatically generate clinical notes from patient-clinician conversations using AWS HealthScribe

One of the first global big tech companies to jump into the healthcare AI industry was Amazon. Amazon's cloud division, Amazon Web Services (AWS), unveiled a healthcare AI tool called AWS Health Scribe.

Health Scribe is a service that utilizes generative AI to automatically create medical records for patients. It also includes the ability to transcribe, summarize, and analyze conversations between healthcare workers and patients in real time based on voice recognition technology. Below are the detailed features of Health Scribe as revealed by Amazon.

  • Provide a time-stamped, step-by-step consultation history
  • Speaker identification
  • Subjective and objective segment conversation logs.
  • Notes and data summaries
  • See the original record
  • Extract medical terms
  • Mark important parts of a doctor-patient conversation

AWS predicts that this model of combining generative and medical AI will not only alleviate some of the pain points of today's medical practices, but will also help deliver quality care. It would also free up time for doctors who would otherwise have to spend their time on administrative tasks, such as document management, while They say it will revolutionize the documentation process.



Apple has developed HealthKit and ResearchKit for managing personal health data to support medical record management and research. Apple's solutions enable seamless data sharing with healthcare organizations in an environment that focuses on the security and privacy of healthcare data.

Google recently launched Med-PaLM, an AI-powered chatbot that can summarize documents or patient health data, as well as answer medical questions.

MedPharm has already earned a reputation as an expert, with an 85% accuracy rate on mock tests for the U.S. medical licensing exam. Google says it's in the process of adding multimodal capabilities that will allow it to synthesize information from things like X-rays and mammograms to improve treatment outcomes.

👉 Related Post: What is Generative AI?

Diagnostic support


Microsoft’s Nuance uses OpenAI GPT-4 to empower physicians to create clinical notes in seconds
Microsoft’s Nuance uses OpenAI GPT-4 to empower physicians to create clinical notes in seconds | BigTechWire

Microsoft has also dipped its toe into the healthcare AI waters with a service similar to Amazon's. In March, Microsoft launched DAX Express, a medical records application developed through its AI subsidiary. DAX stands for "Dragon Ambient eXperience".

Docs Express is characterized by generating medical records based on GPT-4, the language model of ChatGPT Developer Open AI. This allows doctors to record patient information and minutes faster and more accurately, and to identify and manage various diseases and signs more quickly. In addition, Microsoft's AI utilizes natural language processing techniques to analyze medical record data. This makes it easier for doctors to process various medical reports and records and recommend effective treatment plans and appropriate preventive measures.


Tempus Announces Broad Launch of Tempus One
Tempus Announces Broad Launch of Tempus One | Business Wire

Tempus, an American healthcare startup, uses artificial intelligence to analyze patient and medical data. By using artificial intelligence and machine learning to analyze large amounts of medical and clinical data, Tempus aims to automatically recommend more precise information to doctors and more appropriate medicines and treatments to patients.

On their website, Tempus' CEO explains that "we hope to use all of the data to predict disease severity and variables." They also explain that their goal is to ultimately provide patients and doctors with data on which treatments work best and can be personalized.

Medical AI Applications - Korea

Lunit Insight CXR showing the location of abnormalities observed on a patient's chest X-ray with the probability of each finding
Lunit Insight CXR showing the location of abnormalities observed on a patient's chest X-ray with the probability of each finding (Source: Lunit)

Lunit is a medical artificial intelligence company that develops medical image analysis technology. Lunit provides medical image analysis solutions domestically and internationally, with a particular focus on early detection and classification of diseases such as breast cancer, lung cancer, and craniofacial arthritis.

Lunit aims to support accurate diagnosis through AI-enabled medical image analysis technology. To do so, we leverage deep learning algorithms to analyze medical image data, detect tumors, abnormalities, and diseases, and present the results to healthcare providers. Lunit's solution combines image analysis with AI algorithms to provide the ability to contribute to accurate diagnosis and early prevention.

These achievements demonstrate that Lunit's medical image analysis technology is being recognized and utilized in the medical field. Lunit aims to provide accurate and rapid medical image analysis solutions to support medical decision-making, improve diagnostic accuracy, and provide opportunities for earlier treatment.

Buno's AI-powered fundus image reading assistant, BunoMed Funders AI
Buno's AI-powered fundus image reading assistant, BunoMed Funders AI (Source: Buno receives U.S. patent for AI-powered fundus image reading technology)

VUNO is a medical artificial intelligence company that develops medical image analysis solutions. Its medical solutions are centered around AI technology that analyzes medical images to detect tumors, cancers, and diseases.

Buno utilizes deep learning and machine learning algorithms to analyze medical image data and provide accurate diagnoses. In particular, it focuses on early detection and diagnosis of diseases such as breast cancer, lung cancer, liver cancer, and glaucoma, and utilizes various dimensions of information such as voice, image, and data to do so.

Buno's solutions provide accurate results and decision support to healthcare professionals through image analysis and image interpretation. This improves diagnostic accuracy and efficiency for healthcare providers, and helps provide patients with early detection and personalized treatment opportunities.

Benefits and limitations of healthcare AI applications

Benefits of applying AI in healthcare

Let's summarize why applying AI to the healthcare industry is a good idea, and the benefits for patients and doctors, respectively.

Patient benefits

  • Accurate diagnosis and early detection
  • Personalized care
  • Efficient healthcare

Benefits for physicians

  • Accurate diagnostics and decision making
  • Reduce workload and save time
  • Collaboration and knowledge sharing

Thus, with the adoption of AI, patients can detect diseases at an earlier stage, take preventive measures, and have a better chance of being cured. In addition, the analysis of personal characteristics and medical data can help to ensure optimal treatment, minimize side effects, and improve an individual's health status. Ultimately, AI has the ability to automate and automate processing, which can increase the efficiency of healthcare delivery.

From a physician's perspective, AI analyzes large amounts of data that would otherwise be difficult for a physician to handle and identifies patterns to provide accurate and consistent diagnoses and prognostications. This can help support physician judgment, improve diagnostic accuracy, and optimize treatment effectiveness. AI can give doctors more time at the point of care, allowing them to care for more patients, and increase their focus to improve the efficiency and accuracy of their decision-making.

👉 Related: "Only High-Performance AI Models Increase Diagnostic Accuracy"

Limitations of healthcare AI

1. data quality/quantity

Medical AI relies on large amounts of medical data to analyze, read, and diagnose diseases. It requires a deep understanding of the medical domain to obtain useful and reliable results. Lack of domain knowledge can lead to incorrect data interpretation or inaccurate diagnosis, which can cause serious problems in a series of medical procedures.

2. Privacy issues with data

Additionally, because healthcare data contains sensitive and personally identifiable information, privacy and The importance of data management is being emphasized. However, AI technology developers without a specialized understanding of the healthcare domain may find it difficult to ensure proper privacy and data handling methods. This can lead to risks such as data exposure, information loss, and privacy breaches, and can undermine trust and confidence.

3. AI's reliability and collaboration in healthcare

AI systems need to work closely with doctors, nurses, and medical staff for real-world applications in healthcare. However, if AI systems are designed without an understanding of medical knowledge and clinical competencies, they may face challenges in real-world application and implementation. For example, WATSON, developed by IBM, was touted as an artificial intelligence that could replace doctors. However, when asked why WATSON never really took hold in healthcare, experts point to the fact that IT-based deep learning technologists didn't understand healthcare.

Therefore, accurate building training data is an essential element in ensuring the effective deployment and use of AI systems. There is a growing consensus that these limitations must be overcome if "AI doctors" are to become a reality on the hospital floor in the future. Some organizations have explained that the introduction of untested AI into the medical field could not only jeopardize individual health through inaccurate diagnosis and treatment, but also reinforce healthcare inequality and discrimination.

Conclusion: How to apply healthcare AI effectively?

Medical AI has many benefits, including accurate diagnosis and early detection, personalized treatment, and improved healthcare efficiency. As a result, we're seeing a number of applications of AI in healthcare, including medical treatment, health monitoring, and precision treatment planning. However, despite its many benefits, there are also limitations, such as data protection issues, lack of expert knowledge, and lack of mutual learning.

To overcome these limitations and maximize the benefits, training data is becoming increasingly important. Due to the high sensitivity and complexity of data in the medical field, a sufficient amount of valid data is required. To this end, medical professionals and researchers are actively collaborating to create better medical We need to develop ways to collect and manage data, and we need to construct effective medical learning datasets. 

DataHunt conducts continuous projects on AI training data of sufficient quality, such as coronary medical image data processing and Parkinson's disease diagnosis data construction, and strives to create more accurate and valid medical AI models. I'll explain this in more detail in another blog post.


  1. AI in healthcare
  2. Interview Why has the buzz about medical AI replacing doctors died down?
  3. [Expert Column] Digital Healthcare with Medical AI | Science & Technology Career Trends | W-Bridge
  4. Lunit publishes research in international journal..."Why you should use high-performance AI" + PalmEdaily
  5. AI in Radiology: Pros & Cons, Applications, and 4 Examples
  6. Developing reliable AI tools for healthcare
  7. Arterys
  9. Med-PaLM
  10. Microsoft's Nuance uses OpenAI GPT-4 to empower physicians to create clinical notes in seconds | BigTechWire
  11. Tempus Announces Broad Launch of Tempus One | Business Wire
  12. Lunit AI identifies 10 lung diseases..."at the level of an expert image reader"
  13. Buno receives U.S. patent for AI-powered fundus image reading technology
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