Figures
- FIGURE 1
Brief history of artificial intelligence (AI).
- FIGURE 2
Potential applications of artificial intelligence in thoracic surgery in a clinical pathway. CT: computed tomography. The third image is courtesy of https://pngimage.net/. The fourth image represents the Da Vinci Xi surgical robot system (patient's cart and surgeon's console) from Intuitive Surgical.
Tables
- TABLE 1
Keywords in artificial intelligence
Big data Data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges which makes it difficult to process it using on-hand data management tools or traditional data processing applications. A multitude of technology platforms may be involved in the generation or collection of this data, including network servers, digital imaging and communications in medicine (DICOM images) stored in picture archiving and communication system (PACS), electronic health records (EHRs), genomic data, personal computers, smart phones and mobile applications, and wearable devices and sensors. Artificial intelligence algorithms present the ability to search through such databases and extract the necessary data for specific task. Algorithm A set of rules provided to artificial intelligence that allows the machine to perform certain tasks, such as classification. Machine learning A field of artificial intelligence where a system is taught to interpret various features related to an objective and to flag it when it comes up. It is also trained to make predictions by recognising patterns. Automatic learning enables the machine to improve without continuous support and guidance by employing algorithms based on comparison logic, research and mathematical probability. Classifier A machine learning algorithm that sorts data into predefined categories. It can be: Supervised which means it uses data that has been identified by the programmers to generate predictive models to identify unlabelled data. Often, the algorithm attempts to mimic the ability of a highly trained individual with domain expertise. Unsupervised which requires no data labelling. An output or a target is not defined. The computer looks for patterns or grouping in the data. This approach may be useful in facilitating analysis of big data. Reinforcement learning: the inputs and the outputs pairs are not specified, and the focus of the machine is to perform a task while learning from its own successes and mistakes to improve its performance. Neural network Neural networks mimic human brain function with each neuron performing its own simple calculation and the network formed by all of the neurons multiplying the potential of these calculations. This technology is particularly useful in predictive analysis, image recognition and speech processing. Deep learning A subcategory of machine learning where the system is made of digitised inputs, such as an image or speech, which go through multiple layers (ranging from 5 to 1000) of connected “neurons” (neural network), that progressively detect different features, and ultimately provides an output. The results from the first layer of neurons serve as a point of departure for calculating subsequent results. The connected neurons constitute the neural network with autodidactic quality. The system recognises patterns independently and makes predictions on a large quantity of information. Natural language processing A sub field of artificial intelligence dealing with the way to program computers to process and analyse large amounts of natural language data. Challenges are set by speech recognition, natural language understanding and natural language generation. It employs a sequence of mathematical processes and comparisons to identify the user's input, possibly correcting certain errors or applying synonyms, in order to identify all the information necessary to understand the needs of the user. - TABLE 2
Criteria for public consultation analysis by CNEDiMTS on the use of artificial intelligence (AI) software in medicine
Final use 1. Detail the benefit of the information that will be given by the system relying on AI 2. Describe the characteristics of the population for whom it is intended Description of the learning process for the AI 3. Type of learning method Continuous (autonomous learning and adaptation)?
Initial (algorithm is trained initially with no further updates)?
Or incremental (algorithm updated through learning process)?
4. Describe the model used Supervised?
Semi-supervised?
Unsupervised?
By reinforcement?
5. Describe the algorithm used Classification
Regression
Clustering
6. Describe selection method of the model 7. Describe the different steps of learning phase 8. Describe the strategy for updating the algorithm 9. Describe if necessary in which cases humans intervene in the learning process Detail the data entered in the initial learning phase or data involved for the updates or autonomous learning process Describe the characteristics of the sample from the targeted population, used to develop the model 10. Detail the characteristics of the sample 11. Detail the modalities for separating training data from testing data and validating data 12. Justify representativeness of the sample chosen compared with the targeted population Description of the variables 13. Characteristics of the variables Type
Distribution
14. Origins of the variables and methods of acquisition Detail handling of the data before their use for the learning phase 15. Describe the statistical tests used 16. Describe methods of transformation use for the data 17. Describe handling of missing information 18. Explain detection of erroneous or aberrant data and their handling Detail entry data implicated in decision-making 19. Origins of the variables and methods of acquisition 20. Characteristics of the variables Type
Distribution
Performance 21. Describe and justify the method of measurement adopted for the performance 22. Describe the potential impact of adjustments measures 23. Characterise overfitting and underfitting 24. Describe the methods to handle overfitting and underfitting Validation 25. Describe the methods of validation 26. Report the performance of the algorithms on the data set Resilience of the system 27. Describe the mechanisms set up in order to understand model drift 28. Detail the thresholds chosen 29. Detail if there is a system to detect any anomaly in the entry data implicated in the decision 30. Describe the potential impacts of those aberrant entry data 31. Detail the measure set up in case of model drift 32. Detail the situations susceptible to alter the system function Explainability/interpretability 33. Does the algorithm benefit from a technique of explainability/interpretability for the patients and/or the physicians? 34. Detail the elements of explainability available 35. Identify the influential parameters 36. Detail if the decision-making in the system follows guidelines when they exist
Vol 29 Issue 157
Table of Contents
Artificial intelligence in thoracic surgery: past, present, perspective and limits
