The Use of Artificial Intelligence Algorithms in the Prognosis and Detection of Lymph Node Involvement in Head and Neck Cancer and Possible Impact in the Development of Personalized Therapeutic Strategy: A Systematic Review

Evaluating advanced AI reasoning models: ChatGPT-4.0 and DeepSeek-R1 diagnostic performance in otolaryngology: a comparative analysis

PURPOSE

This study aimed to evaluate the diagnostic accuracy, comprehensiveness, and clinical relevance of two advanced artificial intelligence (AI) models, OpenAI's ChatGPT-4.0 and DeepSeek-R1, in the field of otolaryngology.

METHODS

Five common otolaryngology procedures-adenotonsillectomy, tympanoplasty, endoscopic sinus surgery, parotidectomy, and total laryngectomy-were analyzed through standardized queries posed to both AI models. Because the prompts replicate questions that patients typically search online, our evaluation focuses on patient-facing informational adequacy. Responses were independently evaluated by two study members for accuracy, clinical relevance, and comprehensiveness, with discrepancies resolved through consensus. The analysis included comparison with clinical guidelines.

RESULTS

ChatGPT-4.0 generally provided detailed procedural insights, effectively covering indications, methodologies, risks, and recovery processes. However, it occasionally suggested excessive diagnostic imaging and omitted subtle yet significant surgical nuances. DeepSeek-R1 delivered concise, structured responses clearly categorizing indications, treatment alternatives, and procedural risks. Nonetheless, it frequently lacked detailed elaboration, omitting important surgical techniques and minor complications. For instance, DeepSeek-R1 omitted specifics such as hemostatic techniques in adenotonsillectomy and graft stabilization details in tympanoplasty. Neither model adequately addressed critical elements like comprehensive staging, detailed surgical planning, and long-term recovery nuances, especially for complex procedures such as total laryngectomy.

CONCLUSIONS

Both ChatGPT-4.0 and DeepSeek-R1 demonstrated significant diagnostic potential but revealed limitations in precision, comprehensiveness, and nuanced clinical reasoning. Their clinical utility remains restricted, highlighting a continued need for AI refinement to enhance patient-specific decision-making capabilities in otolaryngology.

Generative Adversarial Networks (GANs) in the Field of Head and Neck Surgery: Current Evidence and Prospects for the Future-A Systematic Review

Background: Generative Adversarial Networks (GANs) are a class of artificial neural networks capable of generating content such as images, text, and sound. For several years already, artificial intelligence algorithms have shown promise as tools in the medical field, particularly in oncology. Generative Adversarial Networks (GANs) represent a new frontier of innovation, as they are revolutionizing artificial content generation, opening opportunities in artificial intelligence and deep learning. Purpose: This systematic review aims to investigate what the stage of development of such technology is in the field of head and neck surgery, offering a general overview of the applications of such algorithms, how they work, and the potential limitations to be overcome in the future. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed in conducting this study, and the PICOS framework was used to formulate the research question. The following databases were evaluated: MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, ClinicalTrials.gov, ScienceDirect, and CINAHL. Results: Out of 700 studies, only 9 were included. Eight applications of GANs in the head and neck region were summarized, including the classification of craniosynostosis, recognition of the presence of chronic sinusitis, diagnosis of radicular cysts in panoramic X-rays, segmentation of craniomaxillofacial bones, reconstruction of bone defects, removal of metal artifacts from CT scans, prediction of the postoperative face, and improvement of the resolution of panoramic X-rays. Conclusions: Generative Adversarial Networks may represent a new evolutionary step in the study of pathology, oncological and otherwise, making the approach to the disease much more precise and personalized.

Joint Expedition: Exploring the Intersection of Digital Health and AI in Precision Medicine with Team Integration

Precision medicine stands as a transformative force in the orbit of healthcare, fundamentally reshaping traditional approaches by customizing therapeutic interventions to align with the distinctive attributes of individual patients [...].