AI has the potential to improve how quickly we're able to access important information and deliver care.
MD Experience Ido Zamberg is a board-certified physician and a Fellow at the Division of Experimental Medicine, McGill University Health Center, Montreal, Canada. About C8 Health's Editorial Process At C8 Health, our unwavering commitment is to provide you with trustworthy healthcare information...
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Editorial PolicyPublished August 2, 2024.
AI has become crucial for many sectors, and the healthcare industry is no exception. As innovation continues to drive medicine forward, investment in AI within the field is growing—estimated to reach $188 billion by 2030. [1]
Various AI algorithms form the backbone of software that helps medical professionals access knowledge, diagnose, and treat patients. Below, we explore types of medical AI, their uses, and vital ethical considerations.
» Improve patient care via an AI-driven knowledge management system
Types of AI in Modern Healthcare
Machine learning
Machine learning is a branch of AI that helps systems learn and improve their performance over time. It's become crucial for managing massive datasets in various industries, including healthcare. [2]
This technology uses three core learning methods:
- Supervised: Trains algorithms on labeled data to predict outcomes or classify information (e.g., image recognition, spam filtering).
- Unsupervised: Reveals patterns in unlabeled data (e.g., customer segmentation, anomaly detection).
- Reinforcement: Helps systems learn the best course of action through trial and error (e.g., game playing, robotics).
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Natural Language Processing (NLP)
NLP helps systems understand, interpret, and generate language. This way, computers can translate, summarize, and answer questions. Systems using this technology relied heavily on hand-crafted rules and linguistic knowledge in the early days with limited success.
But, machine learning brought new ways of handling the design flaws. Systems could now learn from vast data. Finally, transformer models propelled NLP to new heights, leading to better performance. These algorithms look at the entire sentence and understand how all the words relate to each other, translating words accurately.
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Rule-Based Systems
Rule-based systems are based on a set of rules and facts. The rules dictate how to process the facts. They're essentially computerized versions of human expert knowledge in a specific field.
The principle is simple: if a specific condition is met, then they take action. For example, a rule-based medical diagnosis system might have a rule like: "If the patient has a fever, cough, and chest pain, then suspect pneumonia.
These algorithms can become complex and limited when dealing with vast amounts of data or ambiguous situations.
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Applications of AI in Healthcare
Information Retrieval
A centralized knowledge management system can speed up information retrieval in healthcare. This way, medical professionals can quickly find best practices in a single place.
Medical AI technologies can improve the process using NLP. For example, C8 Health uses natural language querying and an AI assistant. Your staff can quickly retrieve information from the database by asking simple questions. There's no need to sift through piles of research.
Boosting Operational Efficiency
Hospitals handle vast amounts of health data daily. They need easy access to necessary knowledge if they're to quickly decide the course of treatment. AI-powered clinical decision software helps by looking through notes, statistics, and documentation. This way, medical professionals get essential information quickly and save time.
Consider the Enhanced Recovery After Surgery (ERAS) protocol. [3] It includes specific guidelines for the preoperative, intraoperative, and postoperative care stages. The goal? Improved patient experience.
Traditionally, ERAS protocols relied on statistical models to look at data in electronic health records. But, a shift towards medical AI technologies has shown breakthroughs in operational efficiency. One study showed that they resulted in better management of length of stay. [4]
» Boost operational efficiency for your healthcare organization
Predicting Outcomes and Diagnosing
Healthcare organizations are increasingly using machine learning to analyze vast amounts of health data. It helps them diagnose diseases, predict their outcome, and choose the best treatment.
For example, machine learning can predict the onset of sepsis up to 48 hours before it occurs. [5] The algorithms do it by looking at many clinical parameters, including vital signs and lab values. This way, doctors can intervene on time and save a patient's life.
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Planning Treatment Via Medical Imaging
Hospitals are performing 3.6 billion medical imaging procedures like MRIs and CT scans annually. [6] This is a massive amount of health data. AI algorithms help by analyzing it so that doctors can plan treatment.
For stroke patients, medical AI can analyze CT scans to figure out the size and location of the lesion.
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Challenges and Ethical Considerations
Medical AI is promising, but it raises some challenges and ethical concerns. You should address them carefully if you're to successfully implement AI into your healthcare organization.
Bias and Fairness
Machine learning models are trained on data, and if the data is biased, the model will preserve those biases. In healthcare, this can cause gaps in care, diagnosis, and treatment.
For example, if a model is trained primarily on data from Caucasian patients, it may not accurately predict outcomes for patients of color. To mitigate this, rigorous data auditing, diverse training datasets, and continuous monitoring of the AI model in healthcare is crucial.
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Privacy and Security
Health data is highly sensitive, and its misuse can have devastating consequences. Protecting patient privacy via encryption, access controls, and data minimization is crucial. Additionally, patients must consent to your using their personal information for medical AI.
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Accountability and Transparency
Machine learning models can be complex and difficult to interpret, leading to concerns about accountability. When a model makes a decision that harms a patient, it can be challenging to determine who is responsible.
Efforts to develop explainable AI in healthcare are underway. Additionally, transparency in the development and deployment of AI technologies is crucial to building trust with patients and medical professionals.
Autonomy and Human Oversight
AI can help with decision-making in healthcare, but it shouldn't replace a human touch. Medical professionals must be the ones responsible for patient care. That's why we need to establish clear guidelines for human oversight and intervention in machine learning-driven processes.
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AI-Powered Efficiency: A New Era for Healthcare
The worlds of tech and healthcare can complement each other well, especially when it comes to working on the vast amount of information that clinicians need to make decisions-and making that information more accessible than ever.
It's important to be careful about using patient information. We need doctors, computer experts, policymakers, and government officials to work together. This way, we can use AI to help patients get better without breaking any rules or being unfair.
We can look forward to a future where healthcare is more efficient, predictive, and personalized as we create more uses for medical AI.
» Simplify your operations with an AI-powered knowledge management system
Disclaimer: Our content serves informational purposes only and shouldn't replace the advice and services of qualified professionals. While we strive for accuracy, we cannot guarantee an entirely error-free experience. C8 Health will not be liable for any losses or damages resulting from the use of our content.
