The History of Artificial Intelligence in Healthcare

The Beginning of Artificial Intelligence 

Humans have always been curious about making machines as intelligent as they are. However, the concept of artificial intelligence began years before the first programmable digital computer was invented. There were only myths, stories, and rumors about the automata (the first self-operating machines) before that.

Later, several great philosophers from China, India, and Greece proposed the idea of mechanizing human thoughts. Several researchers from different fields (mechanical, engineering, psychology, and even political science) worked on this concept, which prepared the groundwork for AI.

However, it was not until the 1950s that someone seriously considered the concept of machine intelligence. That person was Alan Turing, a renowned mathematician and computer scientist from the UK.

In his work “Computing Machinery and Intelligence,” he explored the possibility of machines having thinking ability. He also defined when we can actually say that a machine is thinking. He also answered every objection in his study. His work eventually introduced the word ‘Artificial Intelligence.’ 

During these early years, AI in healthcare was not the primary focus. Instead, scientists and researchers were busy proving machines can think logically and solve problems. 

The 1970s: The Evolution of AI in Healthcare 

After decades, AI started making tentative inroads into industries like healthcare. The first target was to help human medical experts make decisions.

MYCIN was one of the first AI advancements in healthcare to serve this goal somewhat. Developed at Stanford University, the system helps diagnose bacterial infections and recommend appropriate antibiotics. 

How MYCIN Works?

MYCIN was a straightforward engine that only asked the physicians yes/no questions. Based on the answers and a knowledge base of around 600 rules, MYCIN created a list of potential infections. It also identifies the likelihood of each diagnosis, its confidence level, the reasoning behind the ranking, and recommended treatments. 

One of MYCIN’s notable features led to some controversy. This feature manages certainty using "certainty factors," leading to arguments between clinicians and developers. Despite these challenges and controversies, MYCIN laid the foundation for future AI applications in healthcare, demonstrating that machines could aid physicians in making complex decisions.

The Early 2000s: The Age of Data and Machine Learning

The early 2000s brought a turning point in the development of AI in healthcare. Digitalization and exponentially growing data fueled this growth, creating new opportunities for AI applications. Advancements in machine learning (ML) techniques increased their capabilities, switching them from pre-defined rules to fresh and real-time data. 

However, advancements in AI are not the only key development shaping the industry currently. The following developments and projects played a vital role. 

Key Developments

1. The Human Genome Project (2003)

Completed in 2003, the Human Genome Project helped the world understand DNA, opening new possibilities for AI-driven precision medicine. The US government launched this world’s largest collaborative project in the late 1980s. 

The publicly funded project was a 13-year human genome study, providing information on 50,000 to 100,000 genes. 

2. IBM Watson for Healthcare (2011)

In 2011, doctors and researchers received a new and advanced tool to work with massive data from the healthcare industry. IBM Watson is a supercomputer with great processing capabilities, and for healthcare, it is no less than a boon. 

The supercomputer is helping doctors in:

• Diagnosing cancer patients. 
• Recommending medications, therapies, and treatments.
• Identifying new drugs or targets for clinical trials. 
• Identifying mutations in genes indicating the onset of diseases.

The 2020s and Beyond: The Future of AI in Healthcare

As we enter the 2020s, AI is expanding its horizons in the healthcare industry, where it is making a positive impact. This impact is driven by the convergence of data, advanced ML algorithms, and computational power. Let’s look at the advanced form of AI developments in healthcare. 

AI-Powered Virtual Assistants: Virtual health assistants built with AI technology are gradually being used in hospitals and clinics. Besides hospitals, they have entered patients' daily lives with wearable technology. They are helping patients get general medical advice at home, book hospital appointments without assistance, and even locate healthcare services nearby. 

AI in Surgery: Surgeries are now becoming less complex with AI assistance. Robotic surgery augmented by AI helps surgeons operate remotely without losing visibility into the patients' real-time conditions. They can perform even delicate procedures with high accuracy and less risk. 

AI in Drug Discovery: The industry's costly and time-consuming process is producing more effective drugs. The development of AI in healthcare is helping here as well. The new systems help researchers assess the effectiveness of drug compounds in the desired target. They also cut the guesswork on evaluating how the drug will react in the patients’ bodies. With these advancements in AI, drug discovery is becoming a less time-consuming and expensive procedure. 

Conclusion

The history of artificial intelligence in healthcare is a story of innovation, adaptation, and ongoing transformation. The healthcare industry has seen various forms of this technology, from the earliest systems to today’s more advanced and precise AI systems. 

While challenges existed before and will persist in the future, artificial intelligence in healthcare will be a forever thing. Neither patients nor researchers will stop using it, given its growing introduction in the industry. The promising results will further increase the reliance on AI.

Softude helps you in developing advanced healthcare solutions using AI technologies, whether for patient diagnosis or automating administrative tasks.