Implementing artificial intelligence and deep learning in medical consultations
Bibliographic Data
| ID | 22096012 |
|---|---|
| Authors | Jorge Luis Saavedra Rubio (0000-0001-5937-1840, Universidad Carlos III de Madrid), Kenia Caridad Sánchez Espinosa (0000-0003-3657-9440) |
| Year | 2023 |
| Volume | 7 |
| Issue | 1 |
| Publication date | 2023-01-24 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Revista Salud y Desarrollo (JOURNAL) |
| Journal identifiers | ISSN: 2307-3624 • E-ISSN: 2791-0369 |
| Publisher | Instituto Especializado de Profesionales de la Salud (PUBLISHER) |
| DOI | 10.55717/vcvj7303 |
| OpenAlex | W4379742627 |
| Language | EN |
| Citations received | 1 |
| References cited | 2 |
Artificial intelligence and deep learning are valuable computer tools for the healthcare sector. The objective of this review was to demonstrate worldwide experiences with their implementation in medical consultations. A PubMed literature search was conducted, analyzing publications from the last five years. These algorithms allowed for the preparation of medical records, treatment recommendations, assessment of teleconsultation quality, and the use of virtual assistants; thereby, expanding access to healthcare and reducing exposure to diseases in healthcare centers. It is concluded that artificial intelligence and deep learning represent a viable resource for consultation, especially in the face of natural disasters and in hard-to-reach areas. Therefore, the necessary IT infrastructure must be created for their healthcare application
Data science · Deep learning · Health care · Political science · Artificial Intelligence in Healthcare · Artificial Intelligence in Healthcare and Education · Computer Science · COVID-19 diagnosis using AI · Artificial Intelligence
| Citations per year | 0.33 |
|---|---|
| Citation span | 2023 - 2023 (1) |
| Highly cited | No |