AI has long been in existence, but it's only in the last few years that we have seen some tremendous growth in this technology. The kind of potential that this technology holds for the future is incomparable. In the current scenario, we are witnessing a huge gap between the demand for Artificial intelligence developers this is primarily because most of the companies are now emphasizing on the need to have AI-driven technology. When we talk about the use cases of AI across different fields, then we cannot miss the healthcare segment where it is finding optimal usage.

Some worth a watch stat:

Since we will be exploring the avenues of AI in the field of healthcare, it becomes important to know how fast the industry is growing. As per the Grand View Research, the global AI in healthcare market size is going to rise with a CAGR of 41.5% and is expected to reach USD 31.3 billion by 2025. This rise in demand is accredited to various factors like the adoption of precision medicine, the growing importance of big data in healthcare, reducing the cost of hardware. Besides, the potential application of AI-based tools in healthcare has acted as a catalytic factor in making this technology gain so much momentum in recent times, and it is expected to grow in the times to come. 

Some additional information:

1. In 2018, software solutions were the key uses of AI-based applications in the healthcare segment.

2. AI automation of clinical trial process was another key use of AI in the field of healthcare 

3. North America was the place to showcase the highest adoption of AI, and it also had the largest revenue share

4. Asia Pacific region is expected to see the highest growth in AI and its based application. This is primarily because of the startups that are venturing in this field.

5. Companies like NVIDIA Corporation, IBM Corporation, Microsoft, Intel Corporation and DeepMind Technologies are a few of the companies that have shown tremendous growth in the field of AI. 

All this data clearly indicates that we are going to witness a steep rise in the demand for AI-based applications and products. With the growing penetration of AI in the field of healthcare, we can expect the demand for artificial intelligence developers is also going to increase, and so is the demand for artificial intelligence training. Most of the companies motivate their employees to adopt Artificial Intelligence training. In fact, there is an organization that is now looking for AI certification as a key skill while hiring new candidates for a technical job. 

There are several benefits which AI can bring to the healthcare segment, both the caregivers, hospitals and the patients. It enables personalized care, medication to the patients. Moreover, there are certain AI-based tools which are equipped to perform preliminary scanning in case of healthcare emergencies. These become extremely beneficial when there is a shortage of physicians and healthcare professionals. 

Here is how AI can prove helpful in the healthcare segment: 

When we talk about the varied use cases of AI in the field of healthcare, then you must be wondering, how will AI prove beneficial. Here we have enlisted a few of the many ways in which AI is finding applications in the healthcare field:

1. Virtual Assistants

2. Diagnosis

3. Robot-assisted surgery

4. Clinical Trials

5. Connected Machines 

6. Fraud Detection

7. Cybersecurity

8. Dosage Error reduction

There are several ways in which AI can prove to be beneficial like they are less probably of errors, timely diagnosis, faster assistance etc. AI-based applications are pre-programmed, and with the assistance of technologies like machine learning, we can expect them to become better with time.

Here we would like to mention about telehealth which is the latest revolution of AI. We know that artificial intelligence is used to detect high-risk diseases on a larger scale and telehealth tools are used at homes of patients to prevent and treat health-risk situations. It reduced the need for readmission to the hospital. Such tools document different metrics, and in case of risk, the machine will automatically inform the practitioner about the same. This can prove beneficial by saving the life of the patient. 

Conclusion