A Search for Undiagnosed Charcot-Marie-Tooth Disease Among Patients Registered with Unspecified Polyneuropathy in the Danish National Patient Registry

Innovations in Medicine: Exploring ChatGPT's Impact on Rare Disorder Management

Artificial intelligence (AI) is rapidly transforming the field of medicine, announcing a new era of innovation and efficiency. Among AI programs designed for general use, ChatGPT holds a prominent position, using an innovative language model developed by OpenAI. Thanks to the use of deep learning techniques, ChatGPT stands out as an exceptionally viable tool, renowned for generating human-like responses to queries. Various medical specialties, including rheumatology, oncology, psychiatry, internal medicine, and ophthalmology, have been explored for ChatGPT integration, with pilot studies and trials revealing each field's potential benefits and challenges. However, the field of genetics and genetic counseling, as well as that of rare disorders, represents an area suitable for exploration, with its complex datasets and the need for personalized patient care. In this review, we synthesize the wide range of potential applications for ChatGPT in the medical field, highlighting its benefits and limitations. We pay special attention to rare and genetic disorders, aiming to shed light on the future roles of AI-driven chatbots in healthcare. Our goal is to pave the way for a healthcare system that is more knowledgeable, efficient, and centered around patient needs.

Importance of Reporting Complete Procedures of Identifying Patients from the Danish National Patient Registry: The Case of Neonatal Jaundice and Epilepsy

Objective

The Danish National Patient Registry (DNPR) is a valuable resource for medical and epidemiological Research. However, not all research articles fully described procedures they used to identify events. In this study, we compared two approaches in identifying persons with a disease diagnosis using neonatal jaundice and epilepsy as examples.

Methods

A cohort of singletons born alive between the 1st January 1997 and the 30th November 2016 in Denmark was used for this purpose. Diagnostic information for a hospital contact in the registry included a primary diagnosis, secondary diagnoses, referral diagnoses, and additional information to a diagnosis (associated diagnoses), if any. Approach 1 identified patients of interest by considering all diagnostic information with exclusion of referral diagnoses only. Approach 2 identified patients of interest by additionally excluding diagnoses from a hospital contact that were coded with Z00 – Z99 of ICD-10 (for health service on examination and reproduction, etc.) as the main reason of the hospital contact. We presented the proportion of people with a diagnosis of neonatal jaundice and epilepsy by the two approaches and explored the potential explanations for the difference.

Results

For the example of neonatal jaundice, the study population included N=1,186,683 persons. The proportion of children with a diagnosis of neonatal jaundice was 5.5% (n=66,736) by approach 1 and 3.9% (n=45,928) by approach 2. For the example of epilepsy, the study population included N=1,183,273 persons. The proportion of children with a diagnosis of epilepsy were 1.2% (n=14,604) by approach 1 and 0.9% (n=10,441) by approach 2.

Discussion

This study demonstrated that the two approaches identified different proportion of persons with a diagnosis of neonatal jaundice and epilepsy. We advocated researchers report complete procedures of identifying patients for making research findings reproducible and comparable.

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