Hematological malignancy is a highly heterogeneous disease with complex biological characteristics and diverse clinical manifestations. Therefore, precise diagnosis and treatment are crucial and urgently needed. To further improve the accuracy of diagnosis and prognostication and to promote personalized therapy, artificial intelligence (AI) has been increasingly used. This study reviewed literature published in the last 5 years and summarized the application, benefits, and drawbacks of AI in the diagnosis, treatment, and prognosis of hematologic malignancies. Although AI can effectively improve the accuracy of diagnosis and therapy, low-quality data, poor interpretability of the model, and limited clinical transformation have impeded its popularization and application. In the future, the clinical application of AI in hematologic malignancy can be accelerated by establishing standards for clinical data processing, integrating multimodal information for accurate diagnosis and prognostication, and conducting systematic clinical verification of model algorithms.

Hematological malignancy is a highly heterogeneous disease with complex biological characteristics and diverse clinical manifestations. Therefore, precise diagnosis and treatment are crucial and urgently needed. To further improve the accuracy of diagnosis and prognostication and to promote personalized therapy, artificial intelligence (AI) has been increasingly used. This study reviewed literature published in the last 5 years and summarized the application, benefits, and drawbacks of AI in the diagnosis, treatment, and prognosis of hematologic malignancies. Although AI can effectively improve the accuracy of diagnosis and therapy, low-quality data, poor interpretability of the model, and limited clinical transformation have impeded its popularization and application. In the future, the clinical application of AI in hematologic malignancy can be accelerated by establishing standards for clinical data processing, integrating multimodal information for accurate diagnosis and prognostication, and conducting systematic clinical verification of model algorithms.

Kisspeptin is an important neuropeptide regulating female reproductive function. It mainly acts on the upstream of hypothalamic gonadotropin-releasing hormone (GnRH) neurons. It binds to the corresponding receptor GPR54 on GnRH neurons to stimulate the release of GnRH, which leads to a series of subsequent activities of the reproductive axis. However, in recent years, it has been found that Kisspeptin exists not only in the central hypothalamus and pituitary, but also in the peripheral ovary, indicating that Kisspeptin not only regulates the female reproductive function in the central, but also plays a local role in the ovary. Kisspeptin is not only involved in the regulation of oocyte, granulosa cell and luteal function, but also affected by gonadotropin, photoperiod, sympathetic nerve and metabolism. This article reviews the central regulation effect and the local ovarian role of of kisspeptin.

Kisspeptin is an important neuropeptide regulating female reproductive function. It mainly acts on the upstream of hypothalamic gonadotropin-releasing hormone (GnRH) neurons. It binds to the corresponding receptor GPR54 on GnRH neurons to stimulate the release of GnRH, which leads to a series of subsequent activities of the reproductive axis. However, in recent years, it has been found that Kisspeptin exists not only in the central hypothalamus and pituitary, but also in the peripheral ovary, indicating that Kisspeptin not only regulates the female reproductive function in the central, but also plays a local role in the ovary. Kisspeptin is not only involved in the regulation of oocyte, granulosa cell and luteal function, but also affected by gonadotropin, photoperiod, sympathetic nerve and metabolism. This article reviews the central regulation effect and the local ovarian role of of kisspeptin.