How technology will change the Future of Healthcare Software Development?

AI Predictions for 2025: Artificial intelligence has played a vital role in a variety of sectors for decades. However, when the term "artificial intelligence" is used in healthcare, many people think that robots working on humans and doctors become redundant.

This is not the case, artificial intelligence has made significant contributions to the healthcare industry and has been at the forefront of leading healthcare trends

Links to large life-saving data sets and forecasts of future health consequences are only a few of the main contributions.

The AI systems market is expected to reach $6 billion by 2021, according to Frost & Sullivan.

What does this mean for healthcare software development, given such phenomenal growth and future potential opportunities?

We'll look at the role of AI in healthcare software creation and how it can help drive automation in the future in this article.

What is AI?

Artificial intelligence (AI) refers to the use of computer systems to model human intelligence functions.

Acquiring knowledge, learning the criteria for using it, translating it, and self-correcting it are some of these processes.

In healthcare, AI has primarily been used by software developers to create healthcare predictive analytics software that uses natural language processing to transform complex medical data into simple actionable information.

AI was not as famous in the healthcare industry a few decades ago. Dendral, which was created in the 1960s, was the first programme with a problem-solving function. MYCIN, an AI healthcare software that was used to locate severe bacterial infections, was built on the foundation of this programme.

Fast forward to today, and AI has come a long way. About 80% of hospitals in the United States, according to a survey by the US Department of Health and Human Services, use healthcare-related software applications. According to a new survey conducted by Intel and Convergys, more than half of healthcare professionals agree that AI will be widely adopted by 2020.

Applications of AI in Healthcare

In the healthcare sector, AI applications are assisting in resolving the conundrum known as the "iron triangle," in which several overarching variables converge. The following is a list of AI's healthcare benefits:

1. Data Analysis and Management

The healthcare industry generates a vast amount of data on a daily basis. The importance of AI in modern healthcare has grown in tandem with the growing velocity of data and the number of sources from which it is derived.

AI has shown success in assisting with data use, data analysis, visualisation, and decision-making. AI has injected significant advancements across the current healthcare spectrum through the use of machine learning and natural language processing to provide better results.

Researchers have used AI-based software's powerful data processing capabilities to capture, analyse, handle, and store clinical trial data. New successful drugs have been established quickly as a result of this systematic approach.

2. Diagnostics

Diagnostic errors are estimated to be responsible for 10% of patient deaths in the United States, according to the Medicine Division of the National Academies of Science. However, AI applications have excelled in this area because of their ability to categorise data, resulting in better diagnostic outcomes.

The diagnostic applications of AI can be divided into four categories:

Chatbots: Healthcare facilities are employing AI-chatbots with speech recognition capabilities to identify recurrent trends in patients' symptoms in order to develop viable diagnosis, avoid illness, and prescribe effective treatment.

Oncology: Medical photographs, test procedures, and a patient's medical history all provide a wealth of knowledge that physicians may use to diagnose a disease and administer medication. A doctor, on the other hand, is limited by what his or her eyes can see, and may lack the requisite knowledge to paint a good image of what is going on and correctly diagnose a disease.

To diagnose cancer, oncologists use deep learning, which involves feeding a set of images into algorithms that profile and identify irregular characteristics.

Pathology: Pathology is a medical discipline that focuses on the detection of illness by laboratory study of bodily fluids and tissues. Machine learning has been able to enhance efforts and procedures that were formerly the domain of pathologists using microscopes.

Rare Diseases: A variety of facial recognition technologies has been paired with deep machine learning to aid in the diagnosis of rare diseases by medical practitioners. Doctors can identify phenotype that mimic those in unusual genetic disorders by analyzing patient photographs.

The Demand for Healthcare Software Developers

Many businesses have jumped on board the AI bandwagon in order to partake in some of the whims that come with the technology. However, there is one key component missing from this rich AI recipe: healthcare app developers.

Small sub-segments of the healthcare sector exist, such as AI diagnostics, robots, medical record tracking, and equipment software systems.

Both of these are areas that app engineers can take advantage of by developing applications for. This, along with the healthcare industry's ongoing transformation of technologies and operations, means the demand for developers isn't going anywhere anytime soon.

Conclusion

We are living in an incredible era in which technology has enabled us to break down barriers that were once unthinkable only a few decades ago. We have been able to perform miracles thanks to technological advancements in the healthcare sector. We also developed real-life cyborgs with technologically enhanced bodies.

You'll accept that such ingenuity and complexity might have stayed a pipe dream without inventions including AI. And, despite the fact that Artificial Intelligence has been hailed as a panacea for earlier disease diagnosis, precise imaging evaluation, and low-cost research in a variety of clinical fields, the relationship between doctors and patients is still a touchy issue.