Are AI language models such as ChatGPT ready to improve the care of individuals with epilepsy?

Abstract

Epilepsy is a neurological disorder characterized by recurrent seizures, which can significantly impact the quality of life of affected individuals. Fortunately, advances in artificial intelligence (AI) are providing new opportunities to improve the diagnosis and treatment of epilepsy. Briefly, examples of ongoing epilepsy-related AI research include (1) algorithms that can analyze large amounts of electroencephalography (EEG) time-series data to label interictal epileptiform discharges both independently and with human supervision,1, 2 (2) diagnostic biomedical imaging with automated magnetic resonance imaging (MRI)–based lesion detection, surgical decision-making support, and outcome prediction,3, 4 and (3) Clinical Decision Support Systems (CDSS) that use patient data to provide physicians with recommendations based on up-to-date evidence and guidelines for an, overall, improved diagnostic and therapeutic accuracy.5, 6 Language models are often used in chatbots and other conversational systems to generate context-aware human-like text in response to an input prompt from a user. Such models are trained on large data sets of human conversations using machine learning (ML) techniques to learn the patterns and structure of natural language. Various artificial intelligence (AI) language models have been developed since the 1950s, but significant advances have only been made in recent years due to improved ML models paired with an increased availability of large amounts of data and computational resources. Some of the earliest examples of such models include ELIZA, developed in the 1960s (one of the first programs to simulate a patient-doctor relationship), and SHRDLU from the 1970s (a program able to emulate dialogue around a simplified world with a limited number of objects, the “blocks world”).7, 8 However, these early language models were inherently limited in their capabilities and could perform only a narrow range of tasks. In recent years, more complex, large language models have led to significant progress in natural language processing. Several of these AI language models can be used for dialogue, for example, (1) GPT-3 (Generative Pre-trained Transformer 3), a state-of-the-art language model developed by OpenAI that can generate contextual human-like text for a wide range of applications, including dialogues9; (2) DialoGPT, a language model developed by Microsoft that is trained on a large data set of social media comment chains and can generate responses in single-turn conversations10; (3) Meena, a sensible and specific language model developed by Google that is trained on human–human conversations from public-domain social media and can generate responses that are coherent and contextually appropriate11; and (4) XLNet, a language model developed by Google and Carnegie Mellon University that is capable of several language modeling tasks including question answering, natural language inference, sentiment analysis, and document ranking; and many others.12 Mainly such algorithms enable the analysis of free-text electronic medical records and other written materials (e.g., test results and treatment plans) that are otherwise inaccessible without preprocessing and standardization. By analyzing large amounts of free-text medical records, language models can learn to identify and summarize relevant patterns. Possible outcomes are information on identified hierarchical patient subgroups based on seizure patterns, documented treatment options, and outcome parameters.13-15 This structured information could be queried to provide personalized treatment recommendations based on medical history and other relevant factors. For example, by identifying early candidates for epilepsy surgery, language models can help minimize treatment delays and improve patient outcomes.16, 17 Another example of how language models can improve health care are Clinical Decision Support Systems (CDSS) trained to understand and offer natural responses to queries from health care providers. CDSS can provide medical or surgical treatment recommendations, suggest relevant clinical guidelines or protocols, and alert health care providers to potential errors or risks. Similar methods may be used to create virtual assistants for individuals with epilepsy to answer questions and provide easy access to information about their condition, treatment options, and other related topics, including driving, causes of premature death (including sudden unexpected death in epilepsy [SUDEP]), and status epilepticus.18, 19 Overall, AI language models have the future potential to significantly improve the care and management of individuals with epilepsy by providing natural conversational interfaces to both patients and physicians, allowing for easy access to structured information. We tested ChatGPT (ChatGPT Dec 15 Version, available at chat.openai.com, last accessed 01/07/2023 at 9:30 p.m.) for some of the use cases outlined above and provided the prompts used and model responses in Figure 1. First, we assumed the role of an individual with epilepsy taking levetiracetam. The model correctly responded that aggression is a possible side effect and recommended follow-up with the prescribing physician (Figure 1A).20 We then requested an Acute Seizure Action Plan (ASAP), a structured treatment plan used to guide patients and caregivers in the event of an epileptic seizure. The model provided a reasonable first draft in line with expert recommendations (Figure 1B).21 We found this useful to quickly generate general patient-facing informational content, but note that each ASAP should be subject to human review to screen for misinformation, and to personalize the draft to include additional information from the individual's medical history and seizure types. We proceeded to present the model with a short, simplified case study of an individual with treatment-resistant left mesial temporal lobe epilepsy. Of interest, the model correctly integrated the medical history and diagnostic findings, noting that hippocampal sclerosis presents an epileptogenic lesion before proceeding to recommend epilepsy surgery. Although this assessment represents a simplification of phase I presurgical evaluation findings and surgical strategies, the overall recommendation is sound.22 However, limitations became apparent when we informed the model that the previously discussed patient now had additional evidence of right temporal lobe seizure onset. Although the initial response is still appropriate, the following advice is actively harmful (Figure 1D). The model confidently states that the patient's health care team may consider bilateral temporal lobectomy or removal of both temporal lobes and the adjacent frontal and parietal lobes (a procedure incorrectly defined as “hemispherotomy” by the model). Finally, even simple queries for structured information may fail if it concerns particularly specialized or disputed areas of knowledge. In Figure 1E, we queried if there is a relationship between variants in SCN9A and autosomal dominant epilepsy. The positive response was incorrect, likely due to misinformation in the academic literature present in the model's training data. Any relationship between variants in SCN9A and epilepsy has been refuted.23, 24 Previous research, as outlined above, has focused on language models trained on large amounts of public-domain data of general human conversations, commonly involving text messages from social media sites (Twitter, Reddit, Facebook, etc.) and some additional training data from books or academic literature. Indeed, the use cases shown above do not accurately represent the limits of this tool, as it was likely not trained on a sufficiently extensive, high-quality, domain-specific data set. It is important to note that language models cannot easily deal with disputed areas of knowledge and may not provide correct answers when contradictions are present in the input data. In light of these general considerations and the specific use cases outlined above, we argue that oversight from medical professionals will be needed to distill training information, and that all current AI applications need to be utilized in combination with human expertise. This is made immediately relevant by the fact that the broad ethical and legal implications of generative models are subjects of ongoing debate, with developers denying liability that may then fall onto the clinician user. Another important limitation of language models is an issue coined “hallucination,” which describes confidently formulated answers with incorrect or nonsensical content.25 This misinformation is a result of biased training data or mismatches between token encoding and concept representation, and it is particularly difficult to identify. Finally, users should be aware that language models show bias against individuals based on gender, race, or disability.26 This issue is particularly sensitive in epilepsy, where stigma is still prevalent.27 Extraction of structured information from electronic medical records and assistance with simple human-supervised tasks are feasible use-case scenarios. However, these systems will need to be thoroughly tested and rigorously validated before they can be used in clinical care, in line with existing regulations on Software as a Medical Device or AI/ML-Enabled Medical Devices.28 Ultimately, AI language models in epilepsy care will depend on developing robust and reliable systems as per the Ethics Guidelines for Trustworthy Artificial Intelligence,29 driven by community-based data sharing and epilepsy-specific AI research. Outside of the clinical care of patients, several successful applications of language models (e.g., smart data processing, content generation, and sentiment analysis) provide a promising perspective of AI-augmented future clinical practice. To achieve similar success stories with AI language models in epilepsy and general clinical practice, we will need to develop protocols for applying decentralized language learning models (i.e., using federated learning) on distributed identifiable patient data from multiple institutions. These coordinated decentralized language models will take advantage of the collective knowledge and insights of multiple sources, including specialty fields like epilepsy, while protecting patient privacy. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. Christian M Boßelmann: Conceptualization, Writing – original draft; Costin Leu: Writing – review & editing; Dennis Lal: Writing – review & editing, Supervision. None. The authors report no conflicts of interest.