WHEN TO TRUST AN ARTIFICIAL INTELLIGENCE AI MODEL

Because machine-learning models can give false predictions, researchers often equip them with the ability to tell a user how confident they are about a certain decision. This is especially important in high-stake settings, such as when models are used to help identify disease in medical images or filter job applications.

MIT researchers have introduced a new approach that can improve uncertainty estimates in machine-learning models. Their method not only generates more accurate uncertainty estimates than other techniques, but does so more efficiently. This technique could give end users, many of whom lack machine-learning expertise, better information they can use to determine whether to trust a model’s predictions or if the model should be deployed for a particular task.

MULTIPLE MYELOMA , PLITIDEPSIN (APLIDIN® PHARMAMAR) WILL BE RE-EVALUATED BY THE EMA

PharmaMar has received a notification from the European Commission (EC) informing the Company of its decision to revoke the refusal to grant Marketing Authorization for plitidepsin (Aplidin®) in Multiple Myeloma.

According to the communication received, the EC has re-evaluated the criteria applied for the participation of experts in the administrative procedure for the Marketing Authorization of Aplidin, as well as the relevant EMA rules governing conflicts of interest, so that they can ensure the objective impartiality of these experts. Consequently, in order to avoid any doubt as to the objective impartiality of the assessment of the application, the Commission has decided it is appropriate to revoke the decision to refuse Marketing Authorization for Aplidin. The Commission has forwarded to the EMA the opinions of the Committee for Medicinal Products for Human Use (CHMP), to request the reevaluation of the application from the time of the onset of the detected procedural irregularity. 

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