By: Renee Broadbent, MBA, CCSFP, CHC, Chief Information Officer & Information Security Officer
George Beauregard, DO, Chief Population Health Officer
Steve Melinosky, Chief Compliance and Regulatory Officer
From streamlining administrative workflows to detecting diseases earlier than ever before, artificial intelligence (AI) is transforming the healthcare industry at every level. What was once considered futuristic is now becoming standard practice, with AI powering tools that assist in diagnostics, personalized treatment plans, and even predict patient outcomes.
As healthcare systems face growing demands, labor shortages, and the need for cost-effective solutions, AI stands out not as a replacement for human care—but as a powerful ally. This article explores the many ways AI is reshaping healthcare, the challenges that come with its adoption, and what it means for the future of medicine.
What Is Artificial General Intelligence (AGI)?
It’s important to recognize that not all AI is the same. One type, called Artificial General Intelligence (AGI) or “strong AI,” refers to highly autonomous systems capable of performing intellectual tasks across various domains—much like human intelligence. AGI would potentially match or exceed human cognitive abilities in most economically valuable tasks, making it a powerful and somewhat intimidating concept.
One example is self-driving cars, piloted by AGI. It can not only pick you up from the airport and navigate unfamiliar roads for you, but it can also adapt its conversation in real time, answering questions about local culture and geography, and personalizing the experience based on the passenger’s interest.
Other AI systems like LaMDA and GPT-3 excel at generating human-quality text while accomplishing specific tasks such as translating languages and creating different types of content. While they may seem like AGI, it’s important to understand they are not quite the same.
How Many Different Types of AI Exist Today?
Additional types of AI include Artificial Superintelligence (ASI), which is AI that surpasses human intelligence in all areas, including creativity, reasoning, and emotional intelligence—portrayed in fictional examples like HAL 9000 and Skynet.
Next is the Large Language Model (LLM), a type of machine learning designed for natural language tasks like content generation, translation, and chatbot interaction. While powerful, LLMs can reflect inaccuracies and biases in their training data. Examples include GPT-4 (OpenAI), LLaMA (Meta), and Google’s models.
AI-powered avatars are digital representations that mimic users’ appearance, voice, and behavior. They’re used in content creation, advertising, corporate communication, and data search, with top tools including D-ID, Synthesia, and Vidia.
Agentic AI is particularly relevant to healthcare. These systems analyze data from various sources, develop strategies, and complete tasks autonomously. They can assist with travel planning, caregiving, and optimizing operations. In healthcare, they’re used to analyze and predict clinical data at a scale beyond human ability.
Lastly, Machine Learning (ML) enables systems to learn and improve through data exposure, and it’s used in tasks like image recognition, sales forecasting, and big data analysis—making it especially valuable in processing vast healthcare datasets to support decision-making.
What Is Artificial Narrow Intelligence?
Artificial Narrow Intelligence (ANI), also known as weak AI, is the most common type of AI today. It’s designed to perform specific, goal-oriented tasks within clearly defined boundaries using a particular dataset. Examples include digital voice assistants like Siri and Alexa, which help with tasks such as providing directions or controlling smart home functions. Unlike more advanced forms of AI, ANI focuses on a single task and cannot operate beyond its programmed purpose.
There are also recommendation engines. Services like Netflix and Amazon use ANI to suggest movies and products based on user preferences. When you go in and load Netflix, it will suggest movies or a TV series based on your previous viewing. Search engines like Google use ANI to process search queries and provide relevant results. Many organizations use ANI-powered chatbots to handle customer inquiries.”
How Is AI Used in Healthcare Settings?
Artificial intelligence is already playing a significant role in several areas of healthcare today. In diagnostic assistance, especially in radiology and pathology, AI can analyze medical images to improve accuracy and speed. For instance, a study in Nature showed AI enhancing breast cancer screening, and another in Radiology demonstrated reduced diagnosis time for conditions like tension pneumothorax—from 36 to 12 seconds.
AI is also accelerating drug discovery and development by designing new drug molecules, shortening what is traditionally a lengthy process. In personalized medicine, wearable devices monitor patients and provide early warnings for chronic conditions.
In hospital operations, AI is used to optimize workflows, staffing, and resource planning to improve efficiency. AI-powered virtual assistants and chatbots support symptom checking and care navigation through algorithm-driven logic, enhancing patient engagement and support.
Certainly, transcribing live conversations into clinical notes, or ambient listening, takes away the task of having to type on a keyboard or speak into a microphone on your computer. The computer is now equipped to just take the conversation live and transcribe it into clinical notes.
Challenges of Incorporating AI into the Healthcare Environment
Each AI use case carries its own vulnerabilities, largely because AI systems are only as good as the data and instructions they’re given—highlighting the old IT adage: garbage in, garbage out. A recent example involved xAI’s large language model, Grok, which generated offensive content due to being trained on flawed data.
This underscores the ongoing need for the human element in AI usage. Humans must set guardrails, interpret results, and remain aware of their own biases. Additionally, AI systems are vulnerable to cyber threats like viruses and hacking.
Another growing concern is AI hallucination, where an AI fabricates inaccurate information despite being trained on reliable data. As AI systems become more advanced and are given greater decision-making power, the importance of oversight, compliance, and responsible use increases—especially in high-stakes environments like healthcare.
The problem with using AI in healthcare is that we are dealing with humans, and their lives are at stake. If uses AI to build a presentation on finances and it gets it wrong, you risk financial loss. But, in healthcare, there’s an element of human life. It’s a big risk, and that is why it is important to prioritize compliance considerations.
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