Comment on: The AI and I: A Collaboration on Competence
Notice bibliographique
Résumé
We are writing to bring attention to the limitations of using artificial intelligence (AI) in surgery. While AI has shown great potential in various fields, including medical imaging and diagnostics, its use in surgical procedures is still in its infancy and has significant limitations. First, AI algorithms require large amounts of data to be trained and tested, which is often not available in the surgical setting. This means that AI systems may not be able to adapt to the unique and complex situations that arise during surgery. Second, the accuracy and reliability of AI systems in surgery is still uncertain. Despite advances in technology, AI systems are still prone to errors and can miss important details that may have significant consequences during surgery. Finally, AI systems are not able to replace the critical thinking and decision making skills of trained surgeons. Surgeons need to be able to analyze a wide range of factors and make split-second decisions that AI systems may not be able to replicate. Overall, while AI has the potential to assist surgeons, its limitations should be carefully considered before implementing it in the surgical setting. Further research and development is needed to improve the accuracy and reliability of AI systems in surgery. Sincerely, Martin G. Tolsgaard and Lawrence Grierson P.S. We did not write any of this. An AI did and this was (close to) a Turing test. We typed the following into the OpenAI GTP-3 chatbot, which has recently been published:1write a letter about the limitations of AI in surgery in 200 words for a surgical journal from 2 scientists. Would you have noticed? Large language models such as GPT-3 can write manuscripts eloquently as seen above and even perform reasonably well on USMLE exams.2 Examples of super-human performance in medical imaging diagnosis have already been published for several years.3 However, now these models are beginning to carve further into human domains of expertise by imitating clinical reasoning, surgical expertise, and academic writing—something that we consider core to what makes us different from AI. This leads us to question the nature and understanding of competence. How will our understanding of what it means to write well academically or be an expert surgeon change when an AI sometimes surpasses our own performance? Narrowly focusing on limitations or benefits of AI may not advance our understanding of what surgeons should be able to do in the future and how. Instead, we should consider exploring when and under what circumstances human-AI collaboration works, for whom and why. We need to turn the scientific discourse away from focusing on how AI can replace clinicians and instead explore how best to support their learning and performances through collective competence. Yet, this requires us to take the science of learning and clinical reasoning into account, which is rarely considered in existing AI research.4
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,072 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,005 |
| Communication savante | 0,005 | 0,009 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,029 | 0,047 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,029 | 0,017 |
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 source (Gemma direct ou Codex distillé), 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 ».