Artificial intelligence in neurology: opportunities, challenges, and policy implications

Voigtlaender, Sebastian; Pawelczyk, Johannes; Geiger, Mario; Vaios, Eugene J.; Karschnia, Philipp; Cudkowicz, Merit; Dietrich, Jorg; Haraldsen, Ira R. J. Hebold; Feigin, Valery; Owolabi, Mayowa; White, Tara L.; Świeboda, Paweł; Farahany, Nita; Natarajan, Vivek; Winter, Sebastian F.

doi:10.1007/s00415-024-12220-8

Cited by 8 publications

(1 citation statement)

References 92 publications

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“…In recent times, there have been notable advancements in the field of artificial intelligence (AI) and computer vision, which have presented encouraging prospects for the automated diagnosis of skin lesions [3], [4]. These advancements hold the potential to enhance efficiency and accuracy in comparison to conventional diagnostic approaches, while adhering to existing regulations [5].…”

Section: Introductionmentioning

confidence: 99%

Efficient Skin Lesion Detection using YOLOv9 Network

Faruq Aziz,

Saputri

2024

MEDINFTech

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Skin lesion detection plays a crucial role in dermatological diagnosis and treatment. In this study, we propose an efficient approach for skin lesion detection using the YOLOv9 network. Leveraging state-of-the-art deep learning techniques, our model demonstrates robust performance in accurately identifying various skin lesion types, including acne, atopic dermatitis, keratosis pilaris, leprosy, psoriasis, and wart. We conducted comprehensive experiments using a curated dataset comprising 2721 training images, 288 validation images, and 145 test images. The model was trained and evaluated based on standard metrics such as Precision, Recall, and mean Average Precision (mAP). Our results indicate promising detection accuracy, with an overall Precision of 60.5%, Recall of 86.0%, and an mAP of 81.4%. Class-wise analysis reveals varying levels of performance across different disease classes, highlighting the model's proficiency in detecting common dermatological conditions such as acne and wart lesions. Furthermore, we provide insights into potential challenges and limitations, including dataset size and class imbalance, and discuss avenues for future research to address these issues. Our study contributes to the advancement of AI-driven solutions for dermatological diagnosis and underscores the efficacy of the YOLOv9 network in skin lesion detection

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Section: Introductionmentioning

confidence: 99%

Efficient Skin Lesion Detection using YOLOv9 Network

Faruq Aziz,

Saputri

2024

MEDINFTech

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Attitude of aspiring orthopaedic surgeons towards artificial intelligence: a multinational cross-sectional survey study

Pawelczyk,

Kraus,

Eckl

et al. 2024

Arch Orthop Trauma Surg

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Introduction The purpose of this study was to evaluate the perspectives of aspiring orthopaedic surgeons on artificial intelligence (AI), analysing how gender, AI knowledge, and technical inclination influence views on AI. Additionally, the extent to which recent AI advancements sway career decisions was assessed. Materials and methods A digital survey was distributed to student members of orthopaedic societies across Germany, Switzerland, and Austria. Subgroup analyses explored how gender, AI knowledge, and technical inclination shape attitudes towards AI. Results Of 174 total respondents, 86.2% (n = 150) intended to pursue a career in orthopaedic surgery and were included in the analysis. The majority (74.5%) reported ‘basic’ or ‘no’ knowledge about AI. Approximately 29.3% believed AI would significantly impact orthopaedics within 5 years, with another 35.3% projecting 5–10 years. AI was predominantly seen as an assistive tool (77.8%), without significant fear of job displacement. The most valued AI applications were identified as preoperative implant planning (85.3%), administrative tasks (84%), and image analysis (81.3%). Concerns arose regarding skill atrophy due to overreliance (69.3%), liability (68%), and diminished patient interaction (56%). The majority maintained a ‘neutral’ view on AI (53%), though 32.9% were ‘enthusiastic’. A stronger focus on AI in medical education was requested by 81.9%. Most participants (72.8%) felt recent AI advancements did not alter their career decisions towards or away from the orthopaedic specialty. Statistical analysis revealed a significant association between AI literacy (p = 0.015) and technical inclination (p = 0.003). AI literacy did not increase significantly during medical education (p = 0.091). Conclusions Future orthopaedic surgeons exhibit a favourable outlook on AI, foreseeing its significant influence in the near future. AI literacy remains relatively low and showed no improvement during medical school. There is notable demand for improved AI-related education. The choice of orthopaedics as a specialty appears to be robust against the sway of recent AI advancements. Level of evidence Cross-sectional survey study; level IV.

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Besteht ChatGPT die neurologische Facharztprüfung? Eine kritische Betrachtung

Wuchty,

Sellner

2024

psychopraxis. neuropraxis

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ZusammenfassungChat generative pre-trained transformer (ChatGPT) ist ein Chatbot, der künstliche Intelligenz (KI) einsetzt, um mit den Benutzer:innen zu kommunizieren. Im Bereich der klinischen Neurologie könnte hierdurch die Patientenversorgung vor allem bei komplexen oder seltenen Erkrankungen unterstützt werden. Um das Potenzial der KI als Hilfsmittel in der postgraduellen neurologischen Ausbildung zu untersuchen, wurde ChatGPT mit neurologischen Prüfungsfragen auf Facharztniveau getestet. Die Ergebnisse zeigen kongruent das Potenzial der KI-Systeme in der künftigen Unterstützung von Ausbildung und Patientenversorgung auf. Die Komplexität des neurologischen Fachgebiets stellt allerdings nicht nur für Nachwuchsneurolog:innen eine besondere Herausforderung dar. Des Weiteren hat die KI noch relevante Limitationen für die Translation in die klinische Praxis, hierzu zählen „Halluzinationen“ und die mangelnde Beurteilung der Seriosität von Datenquellen.

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Artificial intelligence in neurology: opportunities, challenges, and policy implications

Cited by 8 publications

References 92 publications

Efficient Skin Lesion Detection using YOLOv9 Network

Efficient Skin Lesion Detection using YOLOv9 Network

Attitude of aspiring orthopaedic surgeons towards artificial intelligence: a multinational cross-sectional survey study

Besteht ChatGPT die neurologische Facharztprüfung? Eine kritische Betrachtung

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