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Prediction of hematologic cancers through AI to enhance the precision and efficiency in early detection and management of blood cancers.
Hematologic cancers, including leukemias, lymphomas, and multiple myeloma, are significant causes of mortality globally. These cancers are challenging to detect in their early stages, which limits treatment opportunities.
Leukemia is one of the most prevalent cancers in children and young adults in Latin America. Late diagnosis often leads to more expensive and complex treatments and poorer quality of life for patients [1].
Additionally, multiple myeloma, one of the most prevalent hematologic cancers in Latin America, affects over 130,000 people, with high mortality rates due to late diagnoses [2].
AI-driven predictive models can significantly enhance the accuracy and speed of diagnosing hematologic cancers.
By analyzing large datasets of clinical and genetic information, AI can identify patterns that may be missed by clinicians, enabling earlier, more precise diagnoses.
Integrating AI with conversational assistants provides real-time decision support, helping healthcare professionals make informed choices based on the latest clinical guidelines and research.
Integrates guidelines from the European Hematology Association, which detail care standards, findings from Allart-Vorelli et al. on quality of life in blood cancer, and NICE directives.
Hematologic cancers, including leukemias, lymphomas, and multiple myeloma, are significant causes of mortality globally. These cancers are challenging to detect in their early stages, which limits treatment opportunities.
Leukemia is one of the most prevalent cancers in children and young adults in Latin America. Late diagnosis often leads to more expensive and complex treatments and poorer quality of life for patients [1].
Additionally, multiple myeloma, one of the most prevalent hematologic cancers in Latin America, affects over 130,000 people, with high mortality rates due to late diagnoses [2].
AI-driven predictive models can significantly enhance the accuracy and speed of diagnosing hematologic cancers.
By analyzing large datasets of clinical and genetic information, AI can identify patterns that may be missed by clinicians, enabling earlier, more precise diagnoses.
Integrating AI with conversational assistants provides real-time decision support, helping healthcare professionals make informed choices based on the latest clinical guidelines and research.
Without AI tools, late diagnoses of hematologic cancers result in increased treatment costs—up to 40% more for advanced-stage diagnoses compared to early detection [2].
Additionally, misdiagnoses and delayed treatments lead to higher mortality rates, further escalating healthcare costs.
AI improves early detection, leading to better survival rates for patients with hematologic cancers. It also reduces healthcare costs by minimizing diagnostic errors and optimizing resource use, ultimately benefiting both patients and healthcare systems by enabling faster and more accurate treatment.
Integrates guidelines from the European Hematology Association, which detail care standards, findings from Allart-Vorelli et al. on quality of life in blood cancer, and NICE directives.
