From custom online coaching to gene-based nutrition plans, healthcare is growing more personalized as technology advances. In particular, one promising new field of technology: artificial intelligence (AI) empowers healthcare providers to quickly leverage vast amounts of patient medical data and raise the quality of their care.
Firstly, What is AI?
Artificial intelligence has many definitions. In popular culture, it often plays the role of the heartless machine trying to destroy humanity (see Terminator, The Matrix). However, in scientific reality, AI is simply a set of tools and algorithms. Already, it exists in the consumer market, with AI virtual assistants like Siri and Amazon’s Echo (a.k.a. Alexa) using advanced algorithms to respond to consumer needs.
Since the invention of AI in the 1950s, data scientists and software developers working on AI have pursued a goal partially inspired by popular culture and science fiction: the creation of an AI capable of human-level thought and reasoning. To that end, computer scientists created “machine learning,” which enables AI to improve task performance through a process similar to human learning.
Today, when the mainstream media says “AI,” they usually mean “machine learning.” When they say “machine learning,” they typically mean “deep learning,” which refers to using a neural network.
What is a Neural Network?
The term “neural network” describes an information-processing system that uses working units called “neurons” to compute and transmit data. The human brain employs organic neurons, allowing us to think and reason. Similarly, AI uses artificial neurons, enabling AI like Siri and Alexa to understand complex concepts like human speech.
The Intersection of AI, Graph Data, and Healthcare
Some might wonder, “Why are graphs the ideal way to structure data for AI?” The answer is simple: data is inherently relational, both in graphs and in the real world. Consider a social network like Linkedin. You can view Linkedin users as junctions in a “Linkedin” graph. Each user provides personal information through their posts, which become the relational lines between “user” junctions in the “Linkedin” graph.
Once you understand the benefits of graphs to AI, their applications in healthcare become clearer. Human bodies work by gene expression. When a particular gene is active, it may inhibit or excite the expression of other genes. In this manner, the human body functions as a relational network.
Similar to the human body, hospitals are also relational environments. Patients interact with doctors and nurses, as well as with data-gathering equipment. All of these components tend to interact and affect one another, making them highly relational. Consequently, both hospitals and human bodies are best represented by graph data, which excels at portraying relationships.
AI’s Benefit to Healthcare
In a healthcare context, AI can illuminate the impact of specific changes on patient outcomes. For instance, if a hospital adds a new CT scanner, how will it influence patient outcomes? Will patients move through the system more quickly? Will those patients be healthier when they leave? Or, perhaps, a doctor finds a relationship between a set of genes and susceptibility to a particular disease. AI can analyze relational data from a ptient’s genetic code to predict the likelihood of infection.
Since the onset of the COVID-19 pandemic, healthcare has begun evolving at an unprecedented pace. Healthcare providers, AI software developers, and AI consulting firms are collaborating to expedite understanding of COVID-19, find treatments, and develop vaccines.
Peri- and post-pandemic, we expect a new wave of data-driven solutions to launch the global healthcare industry into the future of technology. To that end, graph data and AI are two critical factors in driving technological advancement. Healthcare providers who acquire these crucial technologies will find themselves at the vanguard of modernization.
If you enjoyed this series on AI and healthcare, download our eBook “Turbocharge Your Healthcare Software with Graph Data and Artificial Intelligence” below!