Updated: Jun 28
4 applications of AI in minimally invasive surgery
Robotic Assistance in Surgery
One of the most notable applications of AI in minimally invasive surgery is the use of robotic systems. These advanced robots are equipped with AI algorithms that enable them to interpret real-time data, adapt to the surgeon's movements, and enhance overall precision.
Image Recognition and Analysis
AI-powered image recognition and analysis play a crucial role in MIS. Surgeons rely on high-resolution imaging systems to navigate through the body during minimally invasive procedures.
AI algorithms can analyze these images in real time, identifying critical structures, and anomalies, and providing augmented visualization. This assists surgeons in making informed decisions, reducing the risk of errors, and improving the overall safety of the surgery.
Predictive Analytics for Patient Outcomes
AI's predictive analytics capabilities are being leveraged to assess patient data and predict potential outcomes before, during, and after surgery. By analyzing vast amounts of historical data, AI algorithms can identify patterns and trends that may impact patient recovery.
This information allows surgeons to tailor their approach based on individual patient characteristics, optimizing the chances of successful outcomes and minimizing complications.
Smart Surgical Instruments
Intelligent surgical instruments embedded with AI can adapt to different tissues and provide feedback to surgeons in real-time.
For instance, smart scalpels equipped with sensors can detect tissue types and alert surgeons to potential complications, ensuring a more precise and controlled surgical process.
These advancements not only enhance the precision and efficiency of procedures but also contribute to improved patient outcomes and reduced recovery times.
Updated: Jun 28
Generative AI (GenAI) is a type of artificial intelligence (AI) that can create new content based on what it has learned from existing content.
The new generated content can be text, images, video, audio, or code.
When given a textual prompt, GenAI uses a statistical model (ML) to predict what an expected response might be, and this generates new content.
The most prominent examples that originally triggered the mass interest in generative AI are ChatGPT, DALL-E, Google Bard, and Bing Chat.
Applications of generative AI in medicine
Enhancing medical imaging
Diagnosis of diseases
Personalized medical chatbots
Personalized treatment plans
Medical research and knowledge generation
Medical simulation
Clinical documentation
Risk prediction
Challenges of generative AI in medicine
Acquiring large dataset for training
Reliability and accuracy
Privacy and data security
Ambiguity and interpretability
Future perspective of GenAI in medicine
Multimodal GenAI: AI can integrate multiple modalities, including genetic data, clinical notes, imaging, and sensor data.
Continual learning and adaptive systems
Integration with Big Data and electronic health records
Interactive AI
Generative AI can help doctors make more accurate diagnoses, discover new treatments, and provide personalized care to patients.
However, careful attention must be given to the challenges and ethical considerations of implementing generative AI in medicine.
Updated: Jun 28
Machine learning’s growing popularity is primarily due to an increase in data availability (Big data) and advancements in technology.
Faster machines and smarter algorithms are implemented daily, even in surgery.
Surgeons may increasingly find themselves in the dilemma of acting on the clinical information provided by an algorithm without understanding how it works, that's why it is important to be educated about machine learning.
What is Machine Learning?
Machine learning (ML) is a field of computer science, that allows computers to “learn” without being explicitly programmed.
Machine learning allows computers to automatically infer patterns from data without explicitly being told what these patterns are.
ML can find patterns that humans can not see, make predictions, and form connections between the vast amount of data, thus ML needs a lot of data to understand and complete its tasks (Big data).
How does Machine Learning work?
The task of making computers learn can be broken down into 7 major steps :
01. Collecting data:
Machines initially learn from the data you give them.
02. Preparing the data:
You put all the data together, you clean it, you visualize it, and you split it into a training set and a testing set.
03. Choosing a model:
You choose a model relevant to the task at hand (speech recognition, image recognition, prediction, etc.).
04. Training the model:
Training is the most important step in machine learning. You pass the prepared data to your ML model to find patterns and make predictions.
The model learns from the data and accomplishes the task set.
Over time, with training, the model gets better at predicting.
05. Evaluating the model:
You test the performance of the model on unseen data (testing data, not trained) to measure how your model will perform and its speed.
06. Parameter tuning:
You adjust the values of parameters present in the model.
07. Making predictions:
You use the model to make predictions on unseen data.
Data collected must come from a reliable source, as it will directly affect the outcome of the model. Good data is relevant and contains very few missing and repeated values.
What are the 4 Categories of Machine Learning?
01. Supervised learning
It is used when we can define the task (we know the result) we want the algorithm to learn on data that we already have.
02. Unsupervised learning
Unsupervised learning is a machine learning model that learns patterns based on unlabeled data. Unlike supervised learning, the result is not known ahead of time.
03. Semi-supervised learning
In semi-supervised learning, a result is known, but the algorithm must figure out how to organize and structure the data to achieve the desired results.
04. Reinforcement learning
Reinforcement learning is a machine learning model that can be broadly described as “learning by doing”.
An “agent” learns to perform a defined task by trial and error until its performance is within a desirable range.
The agent receives positive reinforcement when it performs the task well and negative reinforcement when it performs poorly.
What is Deep Learning?
Deep learning, which is a subcategory of machine learning, provides AI with the ability to mimic a human brain’s neural network, to learn and make decisions without the need for explicit programming.
Artificial neural networks are inspired by the structure and function of the human brain and are made up of interconnected “neurons” that process and transmit information.
Machine learning has much potential in surgery, from preoperative planning, perioperative guidance, surgical robotics, complications prediction, and skill analysis.