Asunto(s)
Carcinoma de Células Escamosas , Inmunohistoquímica , Erupciones Liquenoides , Neoplasias Cutáneas , Humanos , Carcinoma de Células Escamosas/patología , Carcinoma de Células Escamosas/diagnóstico , Neoplasias Cutáneas/patología , Neoplasias Cutáneas/diagnóstico , Erupciones Liquenoides/diagnóstico , Erupciones Liquenoides/patología , Masculino , Persona de Mediana EdadRESUMEN
In the dermatological spectrum of oncologic manifestations, cutaneous metastases from endometrial carcinoma stand as a rarity, given the tumour's predilection for neighbouring uterine regions. We present an exceptional case of a patient in her mid-50s, whereby an endometrial carcinoma, defying conventional pathways, manifested on the skin and nail of her distal fourth finger, an unusual site for cutaneous metastases, with a specific histology of the primary cancer.
Asunto(s)
Neoplasias Endometriales , Neoplasias Cutáneas , Femenino , Humanos , Neoplasias Endometriales/patología , Endometrio/patología , Uñas/patología , Neoplasias Cutáneas/patología , Persona de Mediana EdadRESUMEN
The development of the computer and what is now known as artificial intelligence (AI) has evolved over more than two centuries in a long series of steps. The date of the invention of the first computer is estimated at 1822, when Charles Babbage (1791-1871) developed his first design of a working computer on paper, based mainly on a Jacquard loom. He worked on his project together with Augusta Ada King, Countess Lovelace (née Byron) (Ada Lovelace) (1815-1852), whom he called the "Sorceress of Numbers." This work will present the profile and achievements of Charles Babbage, Augusta Ada King, Countess Lovelace, and Alan Mathison Turing (1912 - 1954), who is considered the father of computer science and artificial intelligence, and then provide an outline of the tumultuous events affecting AI up to the present.
Asunto(s)
Inteligencia Artificial , Inteligencia Artificial/historia , Historia del Siglo XIX , Historia del Siglo XX , Humanos , Dermatología/historiaRESUMEN
Artificial intelligence (AI) can be a powerful tool for data analysis, but it can also mislead investigators, due in part to a fundamental difference between classic data analysis and data analysis using AI. A more or less limited data set is analyzed in classic data analysis, and a hypothesis is generated. That hypothesis is then tested using a separate data set, and the data are examined again. The premise is either accepted or rejected with a value p, indicating that any difference observed is due merely to chance. By contrast, a new hypothesis is generated in AI as each datum is added to the data set. We explore this discrepancy and suggest means to overcome it.