Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal International Journal of Advanced Multidisciplinary
Syahroni Lubis
Yarsi Pratama University, Banten, Indonesia
Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Optimization of Radiodiagnostics Based on Artificial Intelligence to Enhance Clinical Accuracy and Efficiency Erwin Santoso Sugandi; Syahroni Lubis; Viedya Wildan; Edwin Suharlim; Intan Masita Hayati
International Journal of Advanced Multidisciplinary Vol. 4 No. 3 (2025): International Journal of Advanced Multidisciplinary (October - December 2025)
Publisher : Greenation Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/ijam.v4i3.1388

Abstract

The integration of Artificial Intelligence (AI) into radiodiagnostics has emerged as a transformative innovation in medical imaging, offering substantial improvements in diagnostic accuracy, workflow efficiency, and clinical decision-making. Over the past five years, AI-driven technologies such as deep learning, convolutional neural networks (CNNs), and radiomics have shown remarkable potential in automating image interpretation, reducing human error, and supporting early disease detection. According to Liu et al. (2021) and Thrall et al. (2022), AI has demonstrated superior performance in identifying complex imaging patterns in modalities such as CT, MRI, and PET scans, surpassing traditional manual analysis in several diagnostic domains. In the Indonesian context, the adoption of AI in radiodiagnostic practices is still in its early stages, with challenges including limited digital infrastructure, lack of technical expertise, and regulatory uncertainty (Suryaningrum et al., 2023). However, initiatives by the Indonesian Radiology Society and academic institutions have begun to explore pilot implementations and collaborative research with global AI developers (Utama & Raharjo, 2024). These efforts highlight the growing recognition of AI’s capacity to optimize clinical workflow efficiency and improve diagnostic precision. This literature review aims to analyze recent advances in AI-based radiodiagnostics, evaluate their implications for clinical accuracy and operational efficiency, and identify the opportunities and challenges specific to the Indonesian healthcare system. Through a comprehensive synthesis of studies published between 2020 and 2025, this paper discusses key trends, algorithmic approaches, ethical considerations, and policy perspectives necessary to guide the responsible integration of AI into clinical radiology practice.
Ethical and Regulatory Evaluation of Artificial Intelligence in Clinical Radiology Practice Evie Kusmiati; Intan Masita Hayati; Syahroni Lubis; Ikhsan Nurahman; Dede Cahyadi; Mustain Billah Marap
International Journal of Advanced Multidisciplinary Vol. 4 No. 3 (2025): International Journal of Advanced Multidisciplinary (October - December 2025)
Publisher : Greenation Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/ijam.v4i3.1389

Abstract

The use of artificial intelligence (AI) in clinical radiology has grown rapidly in line with digital transformation and the increasing demand for efficiency in healthcare services. However, the rapid adoption of AI has not always been accompanied by adequate ethical and regulatory readiness. This study aims to evaluate the ethical framework and regulatory governance of AI applications in radiology practice, both globally and nationally, through a systematic literature review with a narrative analysis approach. The findings reveal that international bodies such as the WHO, FDA, and the European Union have developed ethical guidelines and algorithm audit standards emphasizing transparency, accountability, and patient data security. In contrast, Indonesia has yet to establish specific regulations addressing certification, legal responsibility, and data privacy in medical AI applications. Major challenges include low digital literacy among healthcare professionals, overlapping institutional authorities, and the absence of independent oversight mechanisms. Therefore, the establishment of a national ethical framework, algorithm audit system, and professional training policies is essential to ensure that AI utilization in clinical radiology is conducted safely, transparently, and equitably.
Exploring the Role of Artificial Intelligence in Radiodiagnostics and Radiotherapy: A Literature Review from the Indonesian Context Intan Masita Hayati; Evie Kusmiati; Syahroni Lubis; Anggraeni Pratama Indrianto; Fikar Ryan Wijaya
International Journal of Advanced Multidisciplinary Vol. 4 No. 3 (2025): International Journal of Advanced Multidisciplinary (October - December 2025)
Publisher : Greenation Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/ijam.v4i3.1390

Abstract

The utilization of artificial intelligence (AI) in the fields of radiodiagnostics and radiotherapy has been rapidly advancing worldwide, contributing significantly to improving diagnostic accuracy, work efficiency, and the personalization of cancer therapy. In Indonesia, this development has begun to gain attention through local research, national initiatives, and limited implementation in healthcare facilities. This article aims to review the literature on the use of AI in radiodiagnostics and radiotherapy in Indonesia, covering research trends, application areas, and implementation challenges. The literature search was conducted using international databases (PubMed, Scopus, Google Scholar) and national sources (Garuda, university repositories) with relevant keywords, publication years ranging from 2015 to 2025, and inclusion criteria focused on studies within the Indonesian context. The review findings indicate that AI has been utilized in various aspects of radiodiagnostics, such as mammography analysis, X-rays, and cervical cancer screening, while in radiotherapy, AI has been applied in auto-contouring, dose planning, and quality assurance. Although the potential of AI utilization is highly promising, the main challenges include limited local datasets, infrastructure readiness, regulations, and human resource competence. This article concludes that the development of AI in radiology and radiotherapy in Indonesia requires interdisciplinary collaboration, data standardization, policy support, and large-scale clinical validation studies to ensure safe, effective, and sustainable implementation.
Co-Authors Anggraeni Pratama Indrianto Dede Cahyadi Edwin Suharlim Erwin Santoso Sugandi Evie Kusmiati Fikar Ryan Wijaya Ikhsan Nurahman Intan Masita Hayati Mustain Billah Marap Viedya Wildan