**3.2 Neural networks**

The basic functional unit of the nervous system is the neuron [22]. Neurons function by receiving an input, processing the signal, and generating an output signal [23, 24]. Anatomically speaking, neurons are capable of consolidating up to thousands of neurotransmitter-driven synaptic inputs simultaneously via dendritic extensions, processing a highly transformed version of the original inputs in the soma, and producing a singular output through its axon in the form of an action potential [25]. Importantly, neuronal outputs are not generated at a fixed rate but rather are a function of whether or not the signal summation (excitatory - inhibitory inputs) exceeds a predefined threshold value in order to successfully depolarize the neuron and induce an action potential [26–28]. After traveling through the axon, the action potential signal is transmitted to a multiplicity of neurons synapsed at the axon terminal.

Broadly speaking, artificial neural networks (ANNs) model the biological principles of neuronal signaling in order to stratify and solve complex, nonlinear problems [29]. Considered a subfield of ML, ANNs refer to a digital machine learning algorithm based upon the concept of a biological neuron. Comparatively, where neurons rely on neurotransmitter signaling inputs ANNs leverage binary, categorical, or numeric data sets [5]. Transformation of input signals at the soma into an action potential is akin to an ANN arithmetic-based calculation of inputs into an output [30].

Although the theory underlying ANNs was first developed in the 1980s, premier advances in computational power and training data acquisition at scale have enabled its extensive application in recent years. In neurosurgery, ANNs have grown to be increasingly utilized in diagnostics, prognostics, and management [31]. Deep learning (DL) is yet another class of algorithms increasingly studied in the literature. Although similar to neural networks in principle, the term "deep" refers to the increasing depth of layers present in the neural network – typically accepted to imply at least three layers [32].

The ability to analyze non-linear data by ANNs is ideal for assisting neurosurgeons in clinical decision-making [33]. In particular, ANNs have been widely demonstrated to be superior to traditional analytical methods, especially as it pertains to clinical imaging tasks [34]. Even so, significant challenges still exist which limit the widespread use of ANNs and DL in neurosurgery and medicine at large, including insufficient data, obscured interpretability, reliability of data, high threshold of processing power, and data privacy [3].

#### **3.3 Natural language processing**

Natural language processing (NLP) is another subfield that falls under the scope of ML. As its name implies, the goal of NLP is to better enable human-computer communication by leveraging natural human language to better perform data abstraction processes [35]. In other words, the computer functions to *understand* human-generated text inputs by breaking down sentences into their constituent parts and applying algorithms to derive meaningful outputs. There are two primary divisions within the field of NLP: rules-based models and machine-based models. A rules-based model boasts minimal set-up costs, however is burdensome to scale for large datasets and inflexible as language usage evolves over time; conversely, machine-based models are preferable for large datasets as it can circumvent the rigidity of rules-based model while adapting to evolutions in human lexicon over time [36]. Three methodological approaches that dominate the application of NLP to neurosurgery are classification, annotation, and prediction [37]. Classification involves providing further diagnostic information, and informing the surgeon's decision making in the preoperative phase. Annotation entails automatizing the annotation of a large amount of data (e.g., radiological images) by identifying specific phenotypes related to a disease condition, enabling the NLP algorithm to train on much larger amounts of data and better extrapolate clinical outcomes. Prediction exploits previous data (e.g., free text notes) to predict patient surgical outcomes and enable the neurosurgeon to arrange the resources necessary for their care accordingly. Machine-based NLP as applied to neurosurgery and medicine at scale remains in its infantile stages, though its possibilities rise with the emergence of Large Language Models.

## **3.4 Large language models**

Large Language Models (LLMs) like ChatGPT, developed by OpenAI, are a new wave of AI technology that have profound implications for diverse fields, including healthcare. Educated on a colossal quantity of textual data, these models grasp the delicate intricacies and nuances of human language, thereby equipping them to form pertinent and contextually relevant responses to a broad spectrum of prompts [38].

In March 2023, the performance of ChatGPT and GPT-4 was assessed on a 500-question mock neurosurgical written boards examination. Using Self-Assessment Exam 1 from the American Board of Neurological Surgery (ABNS), Ali et al. fed questions in single best answer, multiple-choice format. ChatGPT and GPT-4 achieved scores of 73.4 and 83.4%, respectively, relative to the question bank user average of 73.7% [39]. Both the question bank users and the LLMs exceeded the previous year's passing threshold of 69%, demonstrating the models' potential technical utility [39].

In a clinical context, including neurosurgery, LLMs could serve multiple purposes. Firstly, they could play a significant role in patient education, simplifying complex neurosurgical procedures, and providing insights into the recovery process in an accessible language [40]. Secondly, these models could help facilitate medical research, from identifying new hypotheses to aiding in clinical decision-making by providing summaries of recent research, medical literature, or guideline updates relevant to specific cases [41].

Another promising application lies in the realm of medical documentation. LLMs could help transcribe doctor-patient conversations, draft surgical reports, or summarize patient histories, thereby streamlining administrative tasks and allowing physicians to focus more on patient care [42]. Continuing Medical Education could also benefit from LLMs. By simulating complex clinical scenarios or generating case studies, these models could serve as an effective teaching tool for medical trainees [43].
