AI in healthcare: How AI Diagnostics are Transforming Medicine industry

As technology advances at an ever-increasing rate, it is difficult to predict what the future holds, especially with the rapid development of artificial intelligence (AI). However, AI in healthcare is already helping to revolutionize this field.

Let's take a look at the current state of AI in healthcare.

Three main areas of AI in healthcare:

1. AI for Disease Diagnosis

Neural networks can be used to analyze medical images, such as X-rays, MRIs, and CT scans, to identify signs of disease. For example, neural networks are already being used to diagnose cancer, cardiovascular disease, and other serious diseases.

2. AI for Disease Treatment

Neural networks can be used to develop new methods of treating diseases. For example, neural networks are being used to develop new drugs and vaccines.

3. AI for Personalized Medicine

Neural networks can be used to personalize medical care for each patient. For example, neural networks can be used to predict the likelihood of a patient developing a disease or to select the optimal treatment plan.

How AI is currently used in healthcare:

In Finland, AI is being used to diagnose breast cancer on mammograms. The system has been shown to increase the accuracy of breast cancer diagnosis by 20%.

In the United States, AI is being used to develop new methods of treating cancer. Companies are working on creating new drugs that could be more effective and less toxic than existing treatments.

In China, AI is being used to personalize medical care for patients with cardiovascular disease. The system helps doctors select the optimal treatment plan for each patient, which reduces the risk of complications and improves the prognosis.

AI has the potential to transform healthcare in numerous ways. By improving the accuracy of diagnosis, developing new treatments, and personalizing medical care, AI can help to save lives, improve quality of life, and reduce costs.

Many of these solutions can be integrated into clinic applications, healthcare applications, telemedicine applications, and so on.

Let's take a closer look at some of the existing solutions on the market from different AI development companies that can already be integrated for your case:

1. Corti.ai

corti Corti.ai is a healthcare AI company founded in 2017 in San Francisco. The company offers two main products:

Corti Triage is an AI system that is used to triage patients by severity. Corti Triage analyzes data from emergency calls and patient consultations to determine the level of medical care required by the patient.
Corti Engage is an AI platform that is used to automate tasks related to healthcare delivery. Corti Engage can be used to create treatment plans, maintain medical records, and ensure the quality of healthcare.

Corti.ai partners with organizations such as the Danish Ministry of Health, Priority Dispatch, and Capital Region of Denmark.

2. Knokcare

Knokcare is a healthcare and digital health solutions company founded in 2015 in Portugal. The company offers a telemedicine platform that allows patients to access healthcare remotely. The platform includes the following features:

Video consultations with doctors. Patients can communicate with doctors in real time via video calls.
Disease diagnosis. The platform uses artificial intelligence to diagnose diseases.
Health monitoring. The platform allows patients to track their health status and exchange information with doctors.

Knokcare partners with organizations such as the Portuguese Ministry of Health, NHS England, and the Brazilian Ministry of Health.

3. Aidoc

aidoc Aidoc is a healthcare AI company founded in 2014 in Israel. The company offers two main products:

Aidoc Chest X-ray AI is an AI system that is used to automatically diagnose lung diseases on chest X-rays. The system has been shown to be 99% accurate in diagnosing pneumonia and other lung diseases.
Aidoc Stroke AI is an AI system that is used to automatically detect strokes on MRI scans of the brain. The system has been shown to be 95% accurate in diagnosing strokes.

Aidoc partners with organizations such as the Israeli Ministry of Health, Cedars-Sinai Medical Center, and the University of Chicago.

4. Aiforia

aiforia Aiforia is a healthcare AI company founded in 2014 in Finland. The company offers a wide range of services in the field of medicine, including:

Disease diagnosis. Aiforia uses AI to analyze medical images, such as X-rays, MRIs, and CT scans, to identify signs of disease. The company offers solutions for diagnosing a wide range of diseases, including cancer, cardiovascular disease, brain diseases, and others.
Disease treatment. Aiforia uses AI to develop new methods of treating diseases. The company is working on creating new drugs and vaccines, as well as new surgical techniques.
Personalized medicine. Aiforia uses AI to personalize medical care for each patient. The company is developing solutions to predict the likelihood of a patient developing a disease or to select the optimal treatment plan.

Aiforia is committed to making healthcare more accessible and effective for people around the world. The company continues to develop new solutions that help improve the quality and availability of healthcare.

Which is better: to create your own neural network or to integrate a ready-made solution?

It depends on your specific case and the tasks you are facing. In general, it is better to use a ready-made solution if it is suitable for you, as you will save a lot of time and budget. If there are no suitable solutions on the market, you will need to create a custom neural network yourself, but you should keep in mind that this will be a resource-intensive part of the project.

Pros of creating your own neural network:

You can customize the network to your specific needs.
You can be more confident in the accuracy of the network.
You can learn more about how neural networks work.

Cons of creating your own neural network:

It can be time-consuming and expensive to develop a neural network.
You need to have a strong understanding of machine learning to create a successful network.
It can be difficult to find data to train the network.

Pros of integrating a ready-made solution:

It is quick and easy to get started.
It can be less expensive than developing your own network.
The network has already been trained on a large dataset, so it is likely to be accurate.

Cons of integrating a ready-made solution:

You may not be able to customize the network to your specific needs.
You may not be able to be as confident in the accuracy of the network as if you had developed it yourself.
You may not learn as much about how neural networks work.

In the end, the best decision for you will depend on your specific needs and budget. If you need a custom-built network that meets your specific requirements, then you will need to create your own network. However, if you are looking for a quick and easy solution that is likely to be accurate, then you may want to consider integrating a ready-made solution.