Notice bibliographique
Résumé
Artificial Intelligence (AI) has taken the world by storm, and like the impact of the internet we saw in the 1990s, it is reshaping the world we live in dramatically.Commonly defined as the ability of computers to perform tasks that are commonly associated with human beings, the definition assumes AI's ability to "learn" and generate answers.However, while there are some similarities in the learning process, there are many more differences, and some of these are key in its impact on Psychiatry as a field.It is indeed a complex area that covers cognition, executive functioning, and judgment, amongst others, but with emotional health being a major component.Emotions cover everything from joy and happiness to misery and sadness and the many different hues in between.How does one tease out the subtle differences between sadness with intact reactivity and sadness with loss of reactivity?Or hostility with little emotional expression and hostility with sarcasm?Are there biological correlates for each of these emotional states, or are they just "normal" expressions of the human mind?We know well how passing an examination gives us joy while failure provides us with a sense of sadness and despondence.There are emotional states that can be very subtle and are not often elicited in routine clinical examinations due to time constraints or cultural factors.A recent study on the use of ChatGPT with a team in Chennai was very revealing about the power of AI in coming to an accurate diagnosis in psychiatry based on DSM, but it was not as impressive in areas that required "complex" learning based on emotional needs when looking at recommendations.[1] As with all technologies, one needs to keep in mind its rapid evolution, and given its ability to learn, the growth will likely be exponential.Whether it will serve to replace individuals who work in the field by becoming easily available or being non-judgmental, less expensive, easily accessible with a smartphone, and with no risk of countertransference is something we can only wait and see, but also it's an area we need to study actively.What will be fascinating is to see if machine learning results in AI developing counter transference in the course of its 'learning' from humans.However, this growth in technology will need safeguards in place to protect it from being misused.As with the internet and the predatory behavior we have seen online, AI can both be a boon and a problem.Internet and telephone scams have become the bane of many countries, along with identity theft.Voice recognition software can easily be used to impersonate people but can also be used in grief work and therapy.AI creates a platform, on the other hand, unlike any we have seen before and can be used to target the vulnerable, as we saw in the 'blue whale challenge' .We need to recognize that the www.archivesbiologicalpsychiatry.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,008 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,003 |
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 ».