AI Use Cases in Healthcare and Trends for 2024

In recent years, fields related to artificial intelligence (AI) developed rapidly and played an important role in many industries. At the same time, the field of healthcare actively developed and this was boosted by the integration of advanced digital technologies.
Medicine is now facing increasing patient demand and needs due to the effects of an aging population, multiple chronic diseases and their complicated methods of treating, increased public health awareness and many more. As a result, healthcare systems will have to deal with a huge number of people with complex needs, i.e. they will have to process a large amount of data. AI in healthcare use cases can come in handy: it can increase the productivity of doctors and efficiency of medical care, as well as optimize many administrative processes. Accenture estimates that AI tools could save the healthcare system about $150 billion a year by 2026, and according to Statista, the global healthcare AI market will reach $188 billion by 2030.
However, it is important to realize that medical data is sensitive in terms of privacy and accuracy of analysis, which means that the software must be strictly regulated according to the quality requirement and country laws.
In this article, we look at some general trends in AI development, the challenges of its implementation in medicine, as well as review some popular examples of AI use cases in healthcare.

The rise of AI and top 3 AI trends

AI-related fields are reviving and getting better at the moment thanks to the development of cloud computing and big data analytics algorithms. AI tools learn from a big bunch of examples, so they can make precise predictions, and cloud technologies allow large AI applications to be run in one place without the need for complex infrastructure deployment.
We can distinguish three varieties among all the AI directions that will be relevant in the near future in general, not only in healthcare.

Automated Machine Learning (AutoML)

It is a technology where machine learning (ML) algorithms are used for specific data. ML tools make decisions, set up and optimize models, and perform time-consuming and repetitive tasks independently. AI & ML use cases in healthcare will have great potential, for example, in making diagnosis based on a large amount of data or in solving the reverse problem, where the system knows the diagnosis and tries to investigate the associated signs of disease. AutoML will also be useful for people in the field of economics and commerce.

Generative AI

The most popular field of the last two years. We think that almost everyone heard about tools like ChatGPT, Midjourney, Jasper.AI, Google Bard and many others. All these generative AI based tools are used to perform all sorts of tasks with instructions. It uses the available data to create a whole new multimedia unit: an image, video, sound or different kinds of text (both human languages and computer code).
Generative AI found its application in marketing and creative purposes, which has led many media businesses to think about regulating these tools. However, it is too early to use generative AI in medicine because the neural network is trained on a large amount of data and does not know what is true and what is false, and the generated data can lead to fatal consequences.

Natural Language Processing (NLP) AI

NLP is a type of artificial intelligence that analyzes, understands, and processes spoken or written human language. Over time, natural language processing based tools are predicted to increase in number, where user interaction will be more intuitive. This type includes the previously mentioned chatbots like ChatGPT and Midjourney, as well as voice assistants from Google, Apple and Amazon.
NLP tools can help doctors in their work by being able to detect and highlight some important information and attributes that, for example, can help the doctor quickly make an accurate diagnosis and prescribe effective treatment.

Challenges in AI implementation in healthcare

AI-based tools have amazing capabilities and can significantly change the work of healthcare professionals for the better, however, there are many legislative, technical and medical research limitations that prevent the mass use cases of AI in healthcare.

• Lack of high-quality medical information. A huge amount of high quality data is required to train neural networks properly. Patient medical data is highly fragmented and sometimes data from different medical organizations is highly inconvenient to share, it becomes difficult to collect it for training and testing AI algorithms. The healthcare system needs to focus on creating medical data standards for this so that they can be used not only for training medical neural networks, but also for more seamless automated work between healthcare organizations.
  
• Legal regulations. Currently, there are several regulations on the storage, collection, and processing of patient personal data. The main laws are the U.S. Health Insurance Portability and Accountability Act (HIPAA) and the European General Data Protection Regulation (GDPR), which protect patient personal data in the U.S. and EU. Of course, these laws came about when artificial intelligence was in its early stages of development (in 1996 and 2018, respectively), but policymakers have recently proposed new laws to regulate AI's handling of high-risk information like medical information. In 2021, the European Commission began work on regulating AI in the EU and issued a Proposal for a Regulation of the European Parliament and of the Council establishing harmonized rules on AI, which will form the basis of a future AI law. According to this proposal, transparency will be one of the main requirements for AI to work with medical data, which is a fundamental problem for such tools.
  
• AI tools transparency. This is a well-known paradox associated with black-box algorithms, which suggests that additional information about AI-based tools has both significant benefits and serious threats. On the one hand, transparency will reduce negative ethical and social consequences, reduce algorithm bias, and increase trust in AI results. But on the other hand, revealing information about tools will make them more vulnerable to attackers and increase the chances of stealing the algorithms of some AI tools that are not open source software. To minimize the negative effects of this paradox, important steps need to be taken in regulating AI and the distribution and protection of information.
  
• Weaknesses in methodological research. Almost all research related to AI in medicine is based on recorded facts about patients. For AI tools to truly provide accurate answers, you need data from prospective studies, i.e. working with current patients in real time using checkups and monitoring with sensors and trackers.
  

Examples of AI use in healthcare

The potential for implementing artificial intelligence in medicine is very big. Algorithms based on the analysis of big pieces of medical data allow for more accurate diagnosis and detection of complex diseases. Unfortunately, the introduction of AI is not so obvious to patients, and many of them do not believe that it will affect the outcome of their treatment for the better. To understand how AI & ML use cases in healthcare, let's look at some impressive examples of how artificial intelligence can improve the work of doctors.

Medical image analysis and recognition

Each of us has a lot of checkups every year, and some of their results are presented graphically: MRI and ultrasound screening, ECG results, X-rays and CT scans. In order to process and group them, a doctor can spend a huge amount of effort and time.
This is where artificial intelligence comes to the aid of cardiologists, radiologists and other doctors, which allows them to quickly analyze a large amount of data, make a more accurate diagnosis and choose the most appropriate method of treatment. With the help of artificial intelligence, it is possible to more accurately find tumors on MRI scans, detect Alzheimer's disease during brain screening, find problems in the human bone system and prevent possible bone fractures.

Analysis of unstructured health records and treatment selection

Artificial intelligence is not only able to analyze patients' graphical data, but it can also analyze their electronic medical records (EMR), which often contain unstructured but very useful information for doctors that helps prescribe effective treatment: the patient's medical history, lifestyle, or genetic information. All this allows the right drug to be selected, especially when making a mistake can be fatal, like in the treatment of complications of diabetes, cancer, or brain and heart diseases.
AI also enables real-time monitoring of patients and tracking of vital parameters, so that doctors can detect disease at an early stage, start treatment and prevent possible complications.

Telemedicine and medical chatbots

Generative AI use cases in healthcare are one of the top trends in 2024 and this technology has many advantages:

• A lot of patients with mild illnesses and those living in small, remote communities can be served with telemedicine.
• Telemedicine helps doctors diagnose patients more quickly because they have many AI and ML-based tools at their disposal to analyze and recognize symptoms of illness.
• Patients can get quick personalized advice about their health in chatbots: they can monitor their health states, learn about medications and their possible side effects, and get recommendations for symptomatic treatment.
• Patients can easily make an appointment with a doctor and prepare all the necessary information for further treatment in the hospital with the help of these technologies.
  

Drugs research & development

Fast-track drug and vaccine development became insanely relevant in the days of COVID-19, when viruses needed to be studied, vaccines had to be clinically tested, and released into production very quickly. Previously, such research was done by trial and error and could take years or even decades and cost billions of dollars. Artificial intelligence significantly reduced the cost and production time by a factor of ten: from decades to months and from billions of dollars to thousands of dollars. AI tools analyze existing drugs and suggest options on how they can be improved to treat diseases more effectively or how they can be modified to treat other diseases. Refining old drugs has proven to be a cheaper process than creating new ones.
AI can help bring new drugs to market much faster, which could help save up to $2.6 billion in drug development costs.

Companies adopting AI in healthcare

Despite technological and legal challenges, some companies have been able to successfully integrate their AI-based tools into the healthcare system. The tools presented below have been developed for a specific group of tasks and diseases, such as recognizing and treating diabetes, heart disease, kidney disease, and eye disease. We will highlight a few companies and research labs that have developed their methods and programs for AI in healthcare use cases.

Google DeepMind

Google DeepMind is a research lab that studies and develops AI solutions. One of the studies was related to the treatment of patients with kidney disease and predicting the treatment of patients in intensive care. The system is based on deep learning technologies that are used to analyze large amounts of medical data to offer patients individualized treatment plans.

Merative (ex. IBM Watson Health)

Merative (ex. IBM Watson Health) is a company that creates medical software based on AI, NLP, ML and big data analytics. Their medical technology helps doctors in creating treatment plans for cancer patients, as well as detecting diseases at the earliest stages.

AliveCor

AliveCor specializes in building deep learning based tools for diagnosing and creating treatment plans for heart disease. They make software to analyze electrocardiograms and also develop hardware to monitor heart health. They were the first company to be authorized to create their own medical band for Apple Watch called KardiaBand that can detect atrial fibrillation, the first cause of stroke.

IDx

IDx-DR is the world's first autonomous diagnostic system for the detection of diabetic retinopathy, a severe complication of diabetes mellitus. The retinal analysis is based on deep learning and the screening results are autonomous so they do not require a physician's description, which allows for screening as part of routine hospital visits.

Conclusion

AI use cases in healthcare can change the way to treat people in many ways:

• It will significantly improve the accuracy of diagnoses
• It will make medicine more accessible to patients
• It will make medical data more understandable and easier to work with
  

Such healthcare progress is hindered by the high cost of implementing and developing new technological products, as well as people's distrust about the use of artificial intelligence in sensitive industries like medicine. Another important reason for the slow AI-technology implementation in medicine is the problem with data processing and interpretation. Most companies and startups are now focused not on creating programs and training AI models, but on collecting user data and structuring it so they can train models on it in the future and create their own products when someone develops effective AI-algorithms especially for healthcare.
However, even now the potential benefits of AI use cases in healthcare are beginning to outweigh the fears and drawbacks, people will change their perceptions of AI, and eventually the technologies of tomorrow will be available to everyone. And one would like to believe that AI in healthcare use cases will help us live long and happy lives.
Medicine and healthcare are just one of the industries where AI-based technologies can be successfully introduced. In other fields, such as art, media and entertainment, AI is also applied to many projects. However, it is important to realize that you should approach the issue of implementing AI in your products responsibly. So if you want to use AI solutions in your business, feel free to contact us and we will be happy to tell you how to implement AI solutions in the most effective way.