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Waisberg E, Ong J, Lee AG. Ethical Considerations of Neuralink and Brain-Computer Interfaces. Ann Biomed Eng 2024; 52:1937-1939. [PMID: 38602573 DOI: 10.1007/s10439-024-03511-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Accepted: 04/03/2024] [Indexed: 04/12/2024]
Abstract
Neuralink is a neurotechnology company founded by Elon Musk in 2016, which has been quietly developing revolutionary technology allowing for ultra-high precision bidirectional communication between external devices and the brain. In this paper, we explore the multifaceted ethical considerations surrounding neural interfaces, analyzing potential societal impacts, risks, and call for a need for responsible innovation. Despite the technological, medical, medicolegal, and ethical challenges ahead, neural interface technology remains extremely promising and has the potential to create a new era of medicine.
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Affiliation(s)
- Ethan Waisberg
- Department of Ophthalmology, University of Cambridge, Cambridge, UK.
| | - Joshua Ong
- Department of Ophthalmology and Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, MI, USA
| | - Andrew G Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, TX, USA
- Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, TX, USA
- The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, TX, USA
- Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, NY, USA
- Department of Ophthalmology, University of Texas Medical Branch, Galveston, TX, USA
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Texas A&M College of Medicine, Bryan, TX, USA
- Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, IA, USA
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Chen X, Zhang W, Xu P, Zhao Z, Zheng Y, Shi D, He M. FFA-GPT: an automated pipeline for fundus fluorescein angiography interpretation and question-answer. NPJ Digit Med 2024; 7:111. [PMID: 38702471 PMCID: PMC11068733 DOI: 10.1038/s41746-024-01101-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 04/04/2024] [Indexed: 05/06/2024] Open
Abstract
Fundus fluorescein angiography (FFA) is a crucial diagnostic tool for chorioretinal diseases, but its interpretation requires significant expertise and time. Prior studies have used Artificial Intelligence (AI)-based systems to assist FFA interpretation, but these systems lack user interaction and comprehensive evaluation by ophthalmologists. Here, we used large language models (LLMs) to develop an automated interpretation pipeline for both report generation and medical question-answering (QA) for FFA images. The pipeline comprises two parts: an image-text alignment module (Bootstrapping Language-Image Pre-training) for report generation and an LLM (Llama 2) for interactive QA. The model was developed using 654,343 FFA images with 9392 reports. It was evaluated both automatically, using language-based and classification-based metrics, and manually by three experienced ophthalmologists. The automatic evaluation of the generated reports demonstrated that the system can generate coherent and comprehensible free-text reports, achieving a BERTScore of 0.70 and F1 scores ranging from 0.64 to 0.82 for detecting top-5 retinal conditions. The manual evaluation revealed acceptable accuracy (68.3%, Kappa 0.746) and completeness (62.3%, Kappa 0.739) of the generated reports. The generated free-form answers were evaluated manually, with the majority meeting the ophthalmologists' criteria (error-free: 70.7%, complete: 84.0%, harmless: 93.7%, satisfied: 65.3%, Kappa: 0.762-0.834). This study introduces an innovative framework that combines multi-modal transformers and LLMs, enhancing ophthalmic image interpretation, and facilitating interactive communications during medical consultation.
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Affiliation(s)
- Xiaolan Chen
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Weiyi Zhang
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Pusheng Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Ziwei Zhao
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Danli Shi
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
- Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
| | - Mingguang He
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong, China
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