Publicação
Accelerating discoveries in cancer nanomedicine using AI
| Resumo: | The integration of artificial intelligence (AI) into cancer nanomedicine is transforming personalized therapy and diagnostics. Focusing on AI-guided design and optimization of nanomedicines, we evaluate how computational technologies can be used to improve targeting precision and therapeutic efficacy by matching treatments to each patient's genetic and phenotypic profile. Surveying contemporary research, this Perspective presents a multidimensional view of AI-enabled nanomedicine that captures its technological, biological, and clinical complexity. Probabilistic and mechanistic models now facilitate the real-time adaptation of nanoparticle formulations. Looking ahead, we anticipate that AI will accelerate discovery and help nanomedicine realize its full potential in precision oncology. In addition to reviewing recent advances, we propose concrete guidelines for embedding AI throughout the nanomedicine pipeline, spanning data curation, model development, preclinical validation, and clinical translation. Finally, we highlight persistent gaps in standardized datasets, model interpretability, and regulatory alignment that must be addressed to achieve widespread clinical impact. |
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| Autores principais: | Guan, Changge |
| Outros Autores: | Mendes, Bárbara B.; Conniot, João; Dias, Ana Laura; Hammad, Layal; Thambi, Thavasyappan; Langer, Robert; Rodrigues, Tiago; Conde, João; de la Fuente-Nunez, Cesar |
| Assunto: | Chemistry (miscellaneous) Biomedical Engineering Biomaterials SDG 3 - Good Health and Well-being |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | recensão |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The integration of artificial intelligence (AI) into cancer nanomedicine is transforming personalized therapy and diagnostics. Focusing on AI-guided design and optimization of nanomedicines, we evaluate how computational technologies can be used to improve targeting precision and therapeutic efficacy by matching treatments to each patient's genetic and phenotypic profile. Surveying contemporary research, this Perspective presents a multidimensional view of AI-enabled nanomedicine that captures its technological, biological, and clinical complexity. Probabilistic and mechanistic models now facilitate the real-time adaptation of nanoparticle formulations. Looking ahead, we anticipate that AI will accelerate discovery and help nanomedicine realize its full potential in precision oncology. In addition to reviewing recent advances, we propose concrete guidelines for embedding AI throughout the nanomedicine pipeline, spanning data curation, model development, preclinical validation, and clinical translation. Finally, we highlight persistent gaps in standardized datasets, model interpretability, and regulatory alignment that must be addressed to achieve widespread clinical impact. |
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