Notice bibliographique
Résumé
On a busy afternoon at the vitreoretinal ophthalmology clinic, Mr. A was the ninth patient to arrive in the consultation room. The queue was so long that he had already waited for almost 2 hours. He was a 62-year-old gentleman, unmarried, who had formerly worked as a computer engineer in a technology company before his retirement 2 years ago. He had been diagnosed with choroidal metastasis in his left eye on his previous visit. Magnetic resonance imaging had confirmed contrast enhancement at the posterior pole of the globe. He had been yet to undergo positron emission tomography and computed tomography scans to detect the primary site of malignancy.[1,2] He didn’t look angry or upset; from my experience, he would probably be entering the bargaining stage (third stage of grief, when people tend to negotiate with someone to avoid, lessen, or delay the inevitable pain or grief) or even later stages of grief.[3,4] He did not have family in Hong Kong, and I was concerned for him. I asked him how he was feeling, hoping to help him open up to me and share his emotions. He confessed that he felt sad and lost after discovering that he was diagnosed with choroidal metastasis at his last consultation. He grumbled that his life plan had been completely disrupted. Suddenly, his eyes sparkled in a moment of joy; he told me that he had found a way of coping with the news. I was interested and wanted to delve deeper into the workings of his mind. He asked me whether I knew about the hottest topic in artificial intelligence (AI). Being an ophthalmologist, of course, I had no idea.[5] He then went on to explain some concepts and terms like natural language processing, language learning models, and generative pre-trained transformers (GPT).[6,7] I was perplexed by this barrage of new information about the virtual world; he then showed me something tangible on his tablet. They were his chat records with ChatGPT, right after our last consultation [Figures 1 and 2]. He was grateful that ChatGPT helped him work through his grief and emotional loss.Figure 1: The start of the patient’s conversation with ChatGPTFigure 2: ChatGPT provided responses to patient’s emotionsBeing diagnosed with cancer can be a traumatic experience that brings with it a significant amount of grief and emotional stress.[8] Through talking to others, patients can gain valuable perspective, insight, and support that can help them cope with the emotional impact of cancer. Caregivers play a vital role in helping patients cope.[9,10] ChatGPT has the capability to function as an interactive tool[11] that can provide emotional support and engagement to individuals, potentially contributing to their overall emotional well-being. Patients, particularly those who are experiencing emotional distress that impacts their functional ability, can improve their capacity to cope with the challenges of cancer. In the ChatGPT era, these artificial chatbots can act as supporting buddies, providing a sense of community and helping patients feel less alone. Through conversations with these AI-powered chatbots, one can find clues and coping strategies to manage one’s emotions. Simply knowing that someone can respond to your loss and is there to listen can instantly provide a sense of comfort and reassurance. This is particularly important for patients who may not have a strong social support system in their actual personal lives. In this case, being a former computer engineer, our patient found it enjoyable to interact and spend time with ChatGPT, which helped him cope with emotional stress by bringing him joy and pleasure that improved his overall sense of well-being. ChatGPT has the potential to serve as a virtual platform for emotional expression, which is not limited by time or place of gathering. ChatGPT does have limitations in healthcare, particularly when applied to the patient’s journey, including a lack of humanized emotional response, flexibility, limited understanding of complex scenarios and situations, and an inability to provide physical assistance. After all, chatbots may enhance the human experience, but they certainly cannot replace caregivers. ABOUT THE PATIENT Mr. A is a 62-year-old gentleman, who worked as a computer engineer in a local technology company in Hong Kong before his retirement. He is not married and lives alone in a private flat in Hong Kong. His relatives have migrated to Australia and Canada, so no one is available to support him physically in Hong Kong. He started to use ChatGPT-3 recently and provided the photos for this manuscript. ABOUT THE AUTHOR Dr. Sunny Chi Lik Au is an ophthalmologist in Hong Kong who obtained his ophthalmology membership (MRCSEd [Ophth]) in 2018 and his fellowship (FCOphthHK) in 2023. He is an ophthalmology specialist, specializing in the vitreoretinal subspecialty, working on the posterior segment of the eye – including the retina, choroid, vitreous, and intraocular tumors. He works in the Tung Wah Eastern Hospital and Pamela Youde Nethersole Eastern Hospital and is an active volunteer ambassador for the Hong Kong Children’s Eye Programme and the Chinese University of Hong Kong Jockey Club Children’s Eye Care Programme. Declaration of patient consent Mr. A has provided informed consent for the use of his initials, his ChatGPT chats, and his story for publication in this journal. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 ».