New Technologies and New Challenges: What Effect Will ChatGPT Have on Plastic Surgery Research?
Notice bibliographique
Résumé
ChatGPT is one of many similar technologies that are poised to have a significant impact on the field of plastic surgery research. While these technologies have the potential to provide valuable insights and accelerate the pace of discovery, they may also bring with them a range of potential problems and challenges that must be carefully considered. As artificial intelligence (AI) language models, these technologies have potential benefits for plastic surgery research. Their primary advantage is the ability to analyze vast data sets and identify patterns and relationships that may not be immediately apparent to researchers.1 These tools may help plastic surgeons identify new risk factors, develop new treatments and interventions, and improve patient outcomes. Other potential benefits include enhanced communication and language translation. However, AI also has the potential to generate misinformation through fake reviews of plastic surgery literature and flawed research findings in plastic surgery.2 One potential problem is the quality of data being analyzed. If the data used to train these models are biased or incomplete, then the insights generated by these models may be similarly flawed. Furthermore, although the technology can generate a large volume of text quickly, it may not provide accurate analysis or interpretation of the data. This could lead to researchers relying on weak data to draw conclusions, inaccurate information being disseminated to the public, and serious consequences for patients and surgeons alike.3 There is also the risk that AI language models could be used to automate research processes that should involve human judgment and expertise, leading to oversimplified or incomplete analyses and undermining the role of experts in plastic surgery. Although these technologies can provide valuable insights and suggestions, they cannot replace the expertise and critical thinking skills of human researchers and surgeons. It is important to consider the broader social and ethical implications of using these technologies in plastic surgery research. These include concerns related to patient privacy and informed consent. If these models are trained on patient data, then there is a risk that patient data could be used subsequently without the patients’ knowledge or consent. This can lead to serious breaches of patient privacy and trust and can undermine the integrity of the research process.4 To address these potential challenges, it is important that the plastic surgery community work together to establish clear guidelines and best practices for their use. This may involve engaging in public debate about the role of technology in plastic surgery research, developing standards for data collection and analysis, keeping patients fully informed about how their data will be used, and establishing protocols for ensuring the accuracy and reliability of the insights generated by these models. As the pace of technological innovation accelerates, it is important to assess the potential impact of these tools, so that they are used in ways that align with the values and principles of the plastic surgery community, to ensure that the use of AI language models in the field of plastic surgery research is beneficial and responsible. DISCLOSURE The authors have no financial disclosures or conflicts of interest to declare. ACKNOWLEDGMENT To illustrate the point of this article, the authors used ChatGPT in the preparation of this article. They would like to thank its developers for the potential to advance the scientific conversation in our field.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».