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All Journal Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Engineering, Mathematics and Computer Science Journal (EMACS)
Faisal Asadi
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Mathematics

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Studi Literatur Regulasi dan Etika Artificial Intelligence (AI) dalam Kebijakan Kedokteran Presisi (Precision Medicine) Asadi, Faisal
JURNAL FASILKOM Vol. 14 No. 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6836

Abstract

Pesatnya perkembangan teknologi Artificial Intelligence (AI) dalam dekade terakhir ini berhasil menjadi pusat perhatian para akademisi dan ilmuan. Tidak hanya akademisi dan ilmuan, tetapi AI juga berhasil merambat ke dalam dunia kesehatan dan kedoketaran. AI in precision medicine menjadi topik yang sangat viral diperbincangkan dalam berbagai forum ilmiah di belahan dunia. Akurasi dan ketapatan AI dalam membantu dokter melakukan diagnosis menjadi suatu topik penelitain yang sedang hangat diperbincangkan karena menyangkut etika dan regulasi dari AI itu sendiri. Maraknya penelitian yang mengkaji tentang regulasi dan etika dari AI dalam kedokteran presisi (precision-medicine) menjadi landasan penelitian ini utnuk meninjau ulang dan melakukan studi literatur yang bersumber pada database jurnal internasional bereputasi yaitu Scopus-database. Dalam studi literatur penelitian ini, kami menemukan beberapa aspek yang perlu diregulasikan dan ditinjau ulang kembali dari segi etika dari AI in precision medicine. Aspek yang ditemukan setelah melakukan review secara comprehensive seperti aspek transparansi dan penjelasan, privasi dan perlindungan data, aspek bias dan fairness, keselamatan dan keamanan, akuntabilitas dan tanggung jawab, serta kolaborasi dan standar global. Beberapa urgensi pentingnya etika AI dalam precision medicine juga dibahas dalam paper penelitian in, seperti kesetaraan dalam akses dan keterjangkauan, keselamatan pasien dan kualitas pelayanan, pengawasan peraturan dan kerangka hukum, efek jangka panjang dan konsekuensi, pendidikan dan kesadaran masyarakat. Selain daripada itu, dalam paper penelitian ini penulis juga memberikan pemaparan terkait trend-research dari AI in precision medicine yang diulas secara detail dan komprehensif.
Leveraging Artificial Intelligence (AI) Methods for Non-Small Cell Lung Cancer (NSCLC) Detection: A Review Faisal Asadi; Ashraf Alif Adillah; Jonathan Lucas Fontana; Michael Dimas Chrispradipta; Mousa Khalil Mousa Ayesh; Wairanatha Halim
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 1 (2026): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i1.15454

Abstract

One of the main causes of cancer-death that significant to global public health concern is lung cancer. NSCLC has been categorized as a burden disease, with an estimated reaching 85% of all lung cancer cases around the world. The problem was that NSCLC disease could only be detected when the disease has grown at a late stage. Therefore, AI technology is also being implemented to handle NSCLC disease. This review discusses how AI has played a role in treating NSCLC disease in the last five years of research journals that collected 5 years between 2019-2024. The database resources are from PubMed, Scopus, and Google Scholar. The process of selecting journal papers was analyzed based on an in-depth understanding of NSCLC disease journals as considered an inclusion criterion. This review used the PRISMA to analysis and review 17 journals. After carrying out the analysis process on the AI-NSCLC journals, we found that AI has been able to help humans respond to cases of NSCLC patients, starting from the detection stage, comprehensive diagnosis, and providing treatment recommendations. Treatments of NSCLC tend to be more personalized and could run more effectively and efficiently based on medical images input into the AI model. However, considering the urgency and vulnerability of the application of these AI models, which will be directly related to human health, the medical images dataset is also quite limited and the biggest challenge for AI-NSCLC.
Co-Authors Ashraf Alif Adillah Jonathan Lucas Fontana Michael Dimas Chrispradipta Mousa Khalil Mousa Ayesh Wairanatha Halim