Letter: The Urgency of Neurosurgical Leadership in the Era of Artificial Intelligence

Abstract

To the Editor: Artificial intelligence's (AI's) increasing viability as an aide to clinical practice marks an inflection point in health care. In the setting of neurosurgery, our group has shown that the general Large Language Model (LLM) GPT-4 (OpenAI), the successor to ChatGPT, outperformed average human users in a mock neurosurgical written boards examination.1 Neurosurgery has a history of embracing new technologies, so the dawning era of AI in medicine will likely play to our strengths. However, neurosurgeons should not just function as passive observers and adopters but rather strive to become leaders in health care systems, industry, and policy to ensure that the benefits of AI for patients and neurosurgical practice are fully realized. As AI's role in health care grows, it is crucial for neurosurgeons to be facile and proficient with these technologies and help shape their impact on patient care. To this end, we outline 6 goals to guide neurosurgeons' response to AI advancements. Goal 1: Neurosurgeons bear the ultimate responsibility for patient care and should continually evaluate AI systems' readiness for patient-facing use. As medical professionals, we have an ethical duty to ensure that AI technologies adhere to the highest safety and efficacy standards, regardless of the technical capabilities of such systems. Neurosurgical cases often have substantive clinical equipoise, and existing AI models can struggle with higher-order management questions or even produce "hallucinations."2,3 Accordingly, our recent work has elucidated that certain AI systems not infrequently confabulate fabricated or incorrect answer rationales, especially when lacking contextual data.3 However, even if AI models did not hallucinate or struggle with higher-order reasoning, the onus would still rest on neurosurgeons to assess suitability for clinical application. Superimposing human judgement remains critical in achieving optimal patient outcomes. For our patients' well-being, it is vital that neurosurgeons continue to rigorously assess AI performance, take ownership of the integration process, and establish guardrails to minimize risk of adverse events. Goal #2: Neurosurgeons should use AI for nonclinical tasks to focus more on patient care. The advent of AI raises the possibility of increased self-sufficiency and efficiency in administrative tasks like the growing burden on physicians of clinical documentation and navigation of prior authorization.4 Recognizing this potential, Epic—a software electronic medical records company holding nearly 80% of Americans' health care data—has already announced a partnership with OpenAI.5 Incorporation of AI into health care may assist with reining in administrative bloat and planning quality improvement initiatives. LLMs may also function as educational resources for learning topics such as health care finance and performance assessment. Finally, AI may improve multidisciplinary collaboration in health care, such as incorporating novel clinical technologies. By integrating AI tools into daily workflows for nonclinical tasks, neurosurgeons can save time and energy for providing the best possible care.6 Goal #3: Neurosurgeons should act as leaders bridging industry and patients. As domain-focused LLMs emerge,7 neurosurgeons should guide the development, testing, and validation of specialized applications of AI for their field. This may involve backend development, frontend optimization, and ethical advising. In compliance with patient privacy laws, neurosurgeons may also proactively create and share open-source registries of clinical data to accelerate the development of tools addressing critical management concerns in our specialty. Goal #4: Neurosurgeons should advocate for appropriate policies, investment, and reimbursement structures to ensure safe and efficient AI adoption in health care. Policy and reimbursement structures can significantly influence the success of emerging neurosurgical technologies. For example, the US Food and Drug Administration's authorization of Gamma Knife and its incorporation into Medicare reimbursement policies accelerated this technology's spread in the 1980–1990s. As AI's potential benefits grow, neurosurgeons must engage in the political arena to facilitate its utilization. This may involve adding an AI-focused arm to existing political apparatuses, such as the American Association of Neurological Surgeons/Congress of Neurological Surgeons Washington Committee, to ensure that the right structures are in place for effective AI integration.8 Nevertheless, while doing so, neurosurgeons must heed prior cautionary tales, such as computer-aided detection in mammograms, which was widely used after gaining the US Food and Drug Administration approval in 1998 and adopted by hospitals to increase profits through extra charges but ultimately failed and even potentially worsened diagnostic accuracy.9 Goal #5: Organized neurosurgical societies should lead in addressing AI advancements. Organized societies may promote standards, develop best practice guidelines, create educational materials for their members, and fund AI research endeavors. Actionable next steps for organized neurosurgery may include forming task forces to study AI implementation, fostering collaboration with AI developers, and advocating for policies that ensure equitable AI integration into health care systems. Goal #6: Neurosurgeons should ensure AI in health care does not worsen existing inefficiencies or inequities in clinical decision-making. Imperfect training processes and data sets can result in biased recommendations, limiting generalizability to diverse patient populations.10,11 As one striking example of how algorithms may inadvertently learn sociocultural biases, Amazon scrapped an experimental AI recruiting tool after it consistently gave female job candidates lower ratings, reflective of historical gender-based hiring disparities captured by the training data set.12 This example underscores the importance of having standards for openness and transparency with respect to the data sets used. For instance, GPT-4 is trained on an unknown data set, which raises concerns about the potential biases it may have inherited. It is not difficult to envision a scenario where technologies trained on biased data may recommend against surgery for patients possessing specific characteristics that have been historically associated with poorer perioperative outcomes.13 Neurosurgeons must identify and address these biases to ensure effective and equitable deployment of AI into clinical care. In this piece, we have outlined just 6 areas for potential technical leadership in the domain of AI. Nevertheless, it remains to be seen how AI can further enable neurosurgeons in nontechnical, adaptive forms of leadership where social and emotional intelligence are increasingly recognized and valued as key drivers for influencing change, innovation, and the social proof needed to bring technological advances to the mainstream. The rise of AI has immeasurable potential impacts on health care and society; neurosurgeons must do our part to align these new tools with our collective needs and interests.

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