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Artificial Intelligence toward Personalized Medicine Gifari, Muhammad Wildan; Samodro, Pugud; Kurniawan, Dhadhang Wahyu
Pharmaceutical Sciences and Research Vol. 8, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In current medical practice when a patient feels symptoms he/she would consult the doctor. The doctor then gives medication in a one-fits-all fashion. However, recent genetics studies had shown that different genetic makeup can results in different effects on medication, so the medication should be customed for every individual. The main idea of “personalized medicine” is to provide the right intervention including medication to the right patient at the right time and dose. With this approach, the medication paradigm would shift from curative to preventive. The rise of personalized medicine had been possible because the information from ever-increasing biomolecular (proteomics, genomics, and other omics) and health-related data are successfully “mined” by Artificial Intelligence (AI) tools. In this paper, we proposed that AI systems toward personalized medicine must have acceptable performance, be readily interpretable by the clinical community, and be validated in a large cohort. We examined a few landmark papers with the keyword “AI for personalized medicine application”; 1) automatic image-based patient classification, 2) automatic gene-based cancer classification, and 3) automatic health-record heart failure with preserved ejection fraction patient phenotyping. All the examples are evaluated by their performance, interpretability, and clinical validity. From the analysis, we concluded that AI for personalized medicine could benefit by five factors: (1) standardization and pooling of genetics and health data, nationally and internationally, (2) the use of multi-modalities data, (3) disease specialist to guide the development of AI model, (4) investigation of AI-finding by clinical community, and (5) follow-up of AI-finding by the large clinical trial.
Etanolic extract of Ling Zhi Mushroom (Ganoderma lucidum) improves lipid profile, CRP and histopathological of liver in dislipidemia model rats Soegianto, Jap Yulius Billy; Samodro, Pugud; Wisesa, Sindhu; Hernayanti, Hernayanti; Setyono, Joko; Arjadi, Fitranto
Biogenesis: Jurnal Ilmiah Biologi Vol 11 No 2 (2023)
Publisher : Department of Biology, Faculty of Sci and Tech, Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/bio.v11i2.38896

Abstract

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Upaya Pencegahan Retinopati Diabetika melalui Edukasi dan Deteksi Dini pada Pasien Diabetes Melitus Tipe 2 di Puskesmas Lumbir Nafiisah, Nafiisah; Setyanto, Muhamad Rifqy; Samodro, Pugud; Harini, Ika Murti; Gumilas, Nur Signa Aini; Mulyanto, Joko
Linggamas: Jurnal Pengabdian Masyarakat Vol 3 No 1 (2025): Linggamas: Jurnal Pengabdian Masyarakat
Publisher : Fakultas Kedokteran Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.linggamas.2025.3.1.18010

Abstract

Retinopati Diabetika (RD) merupakan komplikasi mikrovaskular akibat Diabetes Melitus (DM) yang dapat menyebabkan kebutaan permanen. Di Kecamatan Lumbir, Kabupaten Banyumas, rendahnya pengetahuan masyarakat, keterbatasan akses pelayanan kesehatan, dan kurangnya tenaga medis terlatih menjadi kendala utama dalam pencegahan RD. Kegiatan ini bertujuan meningkatkan pengetahuan pasien DM tipe 2 tentang faktor risiko RD dan melaksanakan deteksi dini di Puskesmas Lumbir melalui edukasi, diskusi, dan pemeriksaan mata. Evaluasi dilakukan dengan pre-test dan post-test, menunjukkan peningkatan sebesar 325 dalam pemahaman peserta mengenai RD dan pentingnya pemeriksaan mata rutin. Hasil pengamatan menunjukkan peningkatan kesadaran peserta tentang RD dan pentingnya pemeriksaan mata rutin.