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What Is Medical AI? Top 4 Medical AI Use Cases
Currently, AI is being used in the medical industry. In recent years, the use of IT in online medical examinations and medical equipment has progressed, and it is certain that this trend will continue in the future. Healthcare without AI utilization will continue to suffer from the current challenges. According to “Transforming Healthcare with AI: The Impact on the Workforce and Organizations” (McKinsey), the demand surge is a result of an amalgamation of relentless factors: the progression of aging populations, evolving patient outlooks, changes in lifestyle preferences, and the continual cycle of innovation, to name a few.
Among these, the repercussions stemming from an aging demographic take center stage. Forecasts indicate that by 2050, a quarter of the populations in Europe and North America will surpass the age of 65—leading to a scenario where healthcare systems must cater to an increased number of individuals with intricate medical requirements. The management of such patients carries substantial costs and necessitates a transition from sporadic, event-based healthcare ideologies to a more proactive approach, one centered around comprehensive, enduring care management
This article will explain:
- Overview of medical AI
- Why AI is being used in the medical industry
- The advantages and disadvantages of using AI in the medical industry
- Medical AI use cases
What is Medical AI?
“Medical AI” is an initiative that utilizes AI (artificial intelligence) to improve the quality of medical care. This technology is used in a variety of fields, including genomic medicine, diagnosis (e.g., interviews and diagnostic imaging), treatment (e.g., surgical support and treatment planning), drug development, and nursing care. In particular, AI-based image diagnosis support is an area in which progress is being made. In other words, medical AI is artificial intelligence technology used in the medical field.
In the medical field, there is a shortage of manpower annually, which places a heavy burden on workers and patients. European countries are considering countermeasures as a big problem. Among them, medical AI is expected.
The application areas of medical AI are:
- Chronic Care Management
- Self-care/Prevention/Wellness
- Triage and diagnosis
- Diagnosis
- Clinical Decision Support
- Care Delivery
And so on.
AI learns huge amounts of data through machine learning and deep learning, so it should be able to handle diagnoses with high accuracy.
Why is the Use of Medical AI so Widespread?
In the medical industry, medical errors due to burnout to a shortage of human resources, such as doctors and nurses, are a problem. The situation is expected to become even more severe in the future, and prompt action is required. Therefore, we will explain why AI is used based on the current situation and issues in the medical industry.
Current Status and Issues of the Medical Industry
(1) Shortage of Medical Personnel
At present, the healthcare workforce in the Netherlands comprises 1.4 million individuals, constituting approximately 14% of the overall working population. However, this figure falls short given the projected reality that one out of every four individuals in Europe will surpass the age of 65 by 2050, necessitating more intricate medical attention. Caring for patients with intricate needs proves costly, prompting a paradigm shift in healthcare from reactive treatment to proactive prevention. Artificial intelligence (AI) offers diverse applications within the healthcare spectrum, ranging from health-promoting chatbots to swift disease diagnosis and tailored treatment prediction.
Jeekel notes, “The Netherlands leads in AI imaging for radiology and pathology diagnostics. This advancement expedites disease diagnosis and has the potential to reduce waiting lists.”
In addition, the lack of young doctors in rural areas has led to regional uneven distribution, and the shortage of human resources has become conspicuous.
Currently, work style reforms are underway, and working hours can be reduced. However, as the number of patients increases, there is a risk that adequate medical care will not be available.
(2) Long Hours of Overwork
Overwork due to a shortage of human resources is also a problem in the medical industry. “Work-Life Balance in European Healthcare” by the European Foundation for the Improvement of Living and Working Conditions (2020) indicated that this report found that overwork is a widespread problem in European healthcare, with 40% of healthcare workers reporting that they work long hours. The report also found that overwork is associated with a number of negative health consequences, such as stress, burnout, and fatigue.
In addition, there are many medical errors due to overwork, which is a severe issue.
Reasons for Using Medical AI
Based on the current situation and issues in the medical field, the use of AI is being promoted as a countermeasure. Medical technology requires advanced knowledge and skills. Human errors and oversights are commonplace.
In fact, the use of AI has begun to attract attention, and discussions are progressing on a national scale. Even now, there are sites where AI is used for diagnosis, and AI-embedded robots are helping to solve the labor shortage. AI has a wide range of application areas, and its use will continue to expand in the future.
Read more: The Future of AI: Exciting Use Cases and Pioneering Adopters
What Can AI Do in the Medical Field? Advantages and Disadvantages
The potential of AI is wide-ranging, and attention is focused on solving current problems. Now, let me explain what AI can do in the medical field.
What Medical AI Can Do
Image Diagnosis Support
By using AI in image diagnosis, it is possible to analyze pathological images such as X-rays and MRIs at high speed. The appropriate diagnosis can be determined quickly, and the burden on the doctor can be reduced.
Gene Therapy
By utilizing AI in genome therapy, it is possible to analyze the patient’s genetic information and quickly analyze the optimal treatment method. By analyzing AI learning data, treatment methods can be diagnosed efficiently.
Diagnosis and Treatment Support
By utilizing AI for diagnosis and treatment support, it is possible to support the identification of diseases and the details of treatment through analysis of natural language processing from patient examination information. It is possible to diagnose with high accuracy from learning data, even rare diseases such as diseases with high frequency and intractable diseases.
New Drug Development
Utilizing AI will help in new drug development. This is because it can analyze huge amounts of drug discovery data. Since it can be expected to shorten the development period and reduce costs compared to conventional methods, it will be possible to develop drugs efficiently.
Nursing Care and Dementia Support
By using AI for nursing care and dementia support, it is possible to analyze the daily life of the elderly and detect dementia early and prevent it from becoming severe. This will reduce the workload of caregivers and nurses. It also helps to improve the safety and quality of life of elderly people and patients with dementia and supports their independence.
Robotic Surgical Assistance
Using AI for surgical support enables safe and accurate surgery. Currently, the shortage of surgeons is also an issue, and we will reduce medical errors due to overwork. It also reduces the burden on the patient by performing safe surgery.
Advantages of Using Medical AI
Next, we will explain the benefits of using AI.
Improving Operational Efficiency in the Medical Field
The first benefit is improved operational efficiency in the medical field. This is because routine work and operations such as image examination can be performed automatically at high speed. It reduces the burden on medical workers who suffer from overwork, saves time, and reduces costs. For example, the creation of documents can be automatically processed, so work can be done accurately and efficiently.
In addition, by using AI to perform image examinations and analyze medical records, it will be possible to quickly present examination results and treatment details.
Reduce Image Diagnosis Errors
The second advantage is the reduction of image diagnosis errors. This is because it can analyze medical images such as X-rays and MRIs and detect small changes that humans cannot notice. Failure to notice such abnormalities can lead to medical errors. For example, it can increase the odds that healthcare professionals will detect subtle changes that are difficult to diagnose early. In fact, image recognition technology has a high accuracy rate of 93.4% for positive predictions and 83.6% for negative predictions.
Diagnosis Supported by Analogy from Big Data
The third benefit is support for medical examinations based on an analogy from big data. This is because a large amount of data such as patient charts can be classified according to regularity and certain criteria. AI can efficiently collect and analyze a large amount of data such as patient medical histories, which will greatly support the medical field. AI will be able to handle large amounts of data, so it will be able to accurately suggest appropriate treatments for individual patients.
Utilization of Genome Analysis
The fourth advantage is the utilization of genome analysis. Genome analysis is a technology that analyzes the genetic information of humans and animals to clarify their characteristics and functions. It takes time for humans to perform genome analysis, but by using AI, highly accurate data analysis can be performed in a short time. Diagnosing the disease tendency and risk from the patient’s genetic information will be possible.
Effective diagnosis will allow us to provide treatment and suitable drugs for each patient.
Disadvantages of Using AI in the Medical Field
Next, I will explain the disadvantages of using AI.
Response to Medical Conditions with Limited Data
The first disadvantage is the response to medical conditions for which there is little data. Because AI learns by accumulating data, the accuracy of unlearned medical conditions deteriorates, resulting in erroneous diagnoses.
In the same way that humans, not just AI, do not know that they have not learned, it is not possible to diagnose a medical condition if there is little data. Over-reliance on AI can lead to medical errors. When using AI, it is necessary for humans to intervene to determine whether the AI’s diagnosis is correct or not.
In the future, how to learn AI will be an issue.
The possibility of Misdiagnosis Due to the Malfunction of AI
The second disadvantage is the possibility of misdiagnosis due to AI malfunction. AI is an excellent technology that makes various tasks more efficient, but AI is not perfect. There are cases where you interpret unknown information without permission and answer.
For example, it may stop due to a bug or malfunction. The use of AI is necessary, but it is necessary to make the final diagnosis based on the doctor’s knowledge and experience, rather than relying on it excessively.
Read more: What is the difference between a core system and a business system and the relationship with ERP?
Top 5 Use Cases of Medical AI Utilization
So far, we have raised the merits of using AI to improve the shortage of human resources in the medical field and overwork. Next, we will explain the use of AI in medical care as the top 4 cases of AI utilization.
The use of AI in medicine is being promoted in the following fields.
AI in Disease Detection and Diagnosis
The first use case is AI in the detection and diagnosis of diseases.
Diagnosis using AI can analyze and predict disease risk, enabling early detection of disease.
By being able to detect the disease at an early stage, the disease can be treated effectively.
For example, MRI image analysis of hepatocellular carcinoma, diagnosis support AI, fundus image diagnosis support system, etc. is appearing.
By discovering and diagnosing diseases using AI, it is possible to grasp the health status of patients at an early stage. As a result, the effectiveness of treatment can be enhanced.
Personalized Disease Treatment
A second use case enables personalized disease treatment.
By processing the patient’s genetic information and personal information with AI, it is possible to propose optimal treatment methods and drugs. This is because it is possible to predict whether a patient is susceptible to illness based on the patient’s medical history and the results of a physical examination.
For example, time-consuming diagnosis can be performed quickly by utilizing genome analysis of AI. It will also be possible to accumulate health information and provide personalized health management advice.
AI in Medical Imaging
The third use case is AI in medical image processing.
Abnormal areas and minute changes can be noticed from the images, enabling rapid and accurate diagnosis. For example, automatic analysis by medical image recognition software. “EIRL Chest Nodule” has a service for discovering pulmonary tuberculosis candidates from chest X-ray images and predicting the progression of Alzheimer’s disease using Fujifilm’s AI technology. AI in medical imaging will enable early detection of abnormalities and effective treatment.
Streamlining Clinical Trials
The fourth use case is the streamlining of clinical trials. This is a technology in which AI analyzes data related to clinical trials and optimizes trial plans and subject selection. By utilizing AI, it is possible to collect huge amounts of data and analyze the information necessary for the success of clinical trials. For example, Hitachi’s IoT platform “Lumada” has been implemented since 2018. By improving efficiency with AI, it will contribute to improving the probability of clinical trial success.
Accelerate Drug Development
The fifth use case is the acceleration of drug development. Japan faces the challenge of soaring medical expenditures in an aging society. In addition, drug development takes a long period of time, and there is a demand for more efficient development and cost reduction. AI can accelerate searching for compounds that cause diseases based on big data and paper data. Accelerating drug development with AI will enable the development of effective treatments for patients with intractable and rare diseases.
Summary
This time, we explained the merits and demerits of AI in medical care, as well as application examples. The current issues facing the medical industry are wide-ranging, and the use of AI is essential to solve them. Here are some of the things you can do with AI.
- Reducing the burden on doctors by supporting image diagnosis
- Presenting optimal treatment methods for patients using genomic therapy
- Identification of diseases from patient information through diagnosis and treatment support
- Cost reduction through new drug development
- Reduce the burden of care and nursing by supporting care and dementia
- Resolving the shortage of surgeons through surgical support
As research and development on the use of AI is progressing on a national scale, the possibility of using AI in future medical care will continue to expand.
If you would like to develop a project or system idea using AI technology in the medical field, please contact Relipa.
Relipa contributes to the growth of the younger generation, creates an environment for active knowledge sharing, and values the attitude of each person being considerate of the other. In doing so, we provide our customers with a reliable and professional service. We understand the concerns and needs of first-time offshore development, inexperienced, and SME customers, so we are confident that Relipa will be the partner you can rely on. Please feel free to contact us if you are a company suffering from a shortage of IT resources.
