The Artificial Intelligence (AI) Model Canvas Framework and Use Cases

Nurcahyo, Aldian; Suroso, Jarot S.; Wang, Gunawan

doi:10.26555/jiteki.v8i1.22206

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…It also endeavors to explore various fields within medical care where ML techniques have shown promising results through systematic examination of existing literature, case studies, and ongoing research projects. The article also highlights examples where ML has demonstrated transformative potential in disease diagnosis [12]- [15], prognosis [16]- [18], personalized treatment [19]- [21], drug discovery [22], [23], and patient management [24], [25], Furthermore, the article seeks to provide an in-depth analysis of the challenges that invariably accompany the integration of ML into medical treatment. These challenges encompass a range of issues, from data privacy concerns and ethical considerations to technical barriers and the need for interpretable models.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning in Healthcare and Medicine: A Review

Furizal,

Ma'arif,

Rifaldi

2023

Journal of Robotics and Control

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This extensive literature review investigates the integration of Machine Learning (ML) into the healthcare sector, uncovering its potential, challenges, and strategic resolutions. The main objective is to comprehensively explore how ML is incorporated into medical practices, demonstrate its impact, and provide relevant solutions. The research motivation stems from the necessity to comprehend the convergence of ML and healthcare services, given its intricate implications. Through meticulous analysis of existing research, this method elucidates the broad spectrum of ML applications in disease prediction and personalized treatment. The research's precision lies in dissecting methodologies, scrutinizing studies, and extrapolating critical insights. The article establishes that ML has succeeded in various aspects of medical care. In certain studies, ML algorithms, especially Convolutional Neural Networks (CNNs), have achieved high accuracy in diagnosing diseases such as lung cancer, colorectal cancer, brain tumors, and breast tumors. Apart from CNNs, other algorithms like SVM, RF, k-NN, and DT have also proven effective. Evaluations based on accuracy and F1-score indicate satisfactory results, with some studies exceeding 90% accuracy. This principal finding underscores the impressive accuracy of ML algorithms in diagnosing diverse medical conditions. This outcome signifies the transformative potential of ML in reshaping conventional diagnostic techniques. Discussions revolve around challenges like data quality, security risks, potential misinterpretations, and obstacles in integrating ML into clinical realms. To mitigate these, multifaceted solutions are proposed, encompassing standardized data formats, robust encryption, model interpretation, clinician training, and stakeholder collaboration.

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