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Enregistrement W4393265869 · doi:10.4103/ijnm.ijnm_67_23

ChatGPT in Nuclear Medicine: Expanding Possibilities and Navigating Challenges

2024· article· en· W4393265869 sur OpenAlexaboutno aff
Bangkim Chandra Khangembam

Notice bibliographique

RevueIndian Journal of Nuclear Medicine · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueArtificial Intelligence in Healthcare and Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineEngineering ethics

Résumé

récupéré en direct d'OpenAlex

I am writing to discuss the potential applications and challenges of utilizing ChatGPT (https://chat.openai.com), a state-of-the-art language model developed by OpenAI, in the field of nuclear medicine. ChatGPT holds promise for enhancing patient education, providing query resolution, offering decision support, supporting research, and facilitating training and education. However, it also presents certain challenges that need to be addressed. One of the significant potentials of ChatGPT lies in patient education.[1] By delivering accurate and personalized information, ChatGPT can help patients better understand nuclear medicine procedures, thereby reducing anxiety and improving the overall patient experience. Patients can engage in conversational exchanges with ChatGPT, allowing them to obtain relevant information about various nuclear medicine techniques and preparations. Furthermore, ChatGPT can address frequently asked questions, educate patients about potential risks and benefits, and explain the purpose of imaging studies or therapies, contributing to informed decision-making. In addition, ChatGPT can serve as a valuable tool for query resolution in nuclear medicine. Health-care professionals can leverage ChatGPT to efficiently address common questions and provide timely responses to patients and colleagues. The model’s ability to understand natural language queries allows it to assist with interpreting nuclear medicine reports, explaining specific radiopharmaceuticals, or clarifying imaging findings.[2] This can save time for health-care providers and improve communication between professionals and patients. ChatGPT also offers the potential for decision support in nuclear medicine. By analyzing complex imaging findings and patient data, ChatGPT can suggest appropriate follow-up procedures, aid in treatment planning, and even generate differential diagnoses.[3] This can assist nuclear medicine practitioners in making more accurate and informed clinical decisions. However, it is important to note that the final decisions should always be made by qualified health-care professionals, and ChatGPT should be considered a supportive tool rather than a substitute for medical expertise.[2,4] Furthermore, ChatGPT can support research efforts in nuclear medicine. The model can generate insights, summarize scientific literature, and help researchers explore new avenues for investigation. It can assist in literature review processes, accelerate data analysis, and potentially contribute to the development of new imaging techniques or treatment modalities.[2,5] In terms of training and education, ChatGPT can simulate a valuable platform for students and residents to practice their diagnostic skills, interpret imaging studies, and deepen their understanding of nuclear medicine principles.[5] This approach allows learners to engage in realistic conversations, receive feedback, and develop their expertise in a safe and controlled environment. However, along with these potentials, there are several challenges that need to be considered. Ensuring the accuracy and reliability of ChatGPT’s responses is crucial.[2,4,5] The model needs to be trained on high-quality, up-to-date data specific to nuclear medicine to minimize misinformation or outdated information being provided. Continual training and validation are necessary to keep the model updated with the latest advancements in the field. Moreover, privacy, ethical, and security concerns must be addressed when using ChatGPT in nuclear medicine. Patient data privacy should be ensured, and compliance with regulatory standards is essential. Safeguards should be implemented to protect sensitive patient information during interactions with ChatGPT. In conclusion, ChatGPT holds significant potential in various applications within nuclear medicine, including patient education, query resolution, decision support, research support, and training and education. While challenges related to accuracy, data quality, privacy, ethics, and security must be addressed, the integration of ChatGPT in nuclear medicine has the potential to enhance patient care, advance research, and improve education in the field. Thank you for considering this letter for publication. Sincerely, Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest. This entire letter including the title was written by ChatGPT in response to the following prompts. 1. Write a letter to the editor in 450 to 500 words on the potential applications and challenges of ChatGPT in Nuclear Medicine. 2. Suggest a title. 3. Give citations in parentheses for every information claimed. Give 5 appropriate references in Vancouver style. It may be noted that the references initially suggested by ChatGPT were fictitious, a term called AI hallucination. Hence, the appropriate references were cited manually.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,028
score de la tête « metaresearch » (Gemma)0,082
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,043
Score d'incertitude au seuil0,147

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0280,082
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0030,002
Études des sciences et des technologies0,0040,007
Communication savante0,0110,030
Science ouverte0,0060,015
Intégrité de la recherche0,0090,010
Charge utile insuffisante (le modèle a refusé de juger)0,0430,024

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.

Tête enseignante Opus0,117
Tête enseignante GPT0,427
Écart entre enseignants0,310 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

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