A hybrid intelligent system for skin disease diagnosis

Dabowsa, Nisreen I. Abo; Amaitik, Nasser; Maatuk, Abdelsalam M.; Aljawarneh, Shadi

doi:10.1109/icengtechnol.2017.8308157

Cited by 22 publications

(14 citation statements)

References 7 publications

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“…The presence of computerized reasoning (AI) as a method for improved medical services offers unprecedented occasions to recuperate patient and clinical group results, decrease costs, etc. The models used are not limited to computerization, such as providing patients, "family" (Musleh et al 2019;Dabowsa et al 2017), and medical service experts for data creation and suggestions as well as disclosure of data for shared evaluation building. AI can also help to recognize the precise demographics or environmental areas where the frequency of illness or high-risk behaviors exists.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, in related work, the techniques like Boltzmann machine, K nearest neighbour (kNN), support vector machine (SVM), decision tree, logistic regression, fuzzy logic, and artificial neural network to diagnose the diseases are presented along with their accuracies. For example, a research study by Dabowsa et al (2017) used a backpropagation neural network in diagnosing skin disease to achieve the highest level of accuracy. The authors used real-world data collected from the dermatology department.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

416

173

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Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

416

173

View full text Add to dashboard Cite

show abstract

“…The cost of developing an information system is reduced as a result of advancements in information technology. The quality of the services offered to patients and the coherence of the patient care process improves when this data is accessed through the EMR system rather than the paper system [9], [10]. The importance of this study stems from its intention to concentrate primarily on the requirement of the acceptance and completion of health information systems and electronic medical records in Bahrain, on the major barriers, which represent a hesitant block on the path of the successful achievement of health information systems and electronic medical records in an effective and efficient way and on a large scale, which would enable health care professionals and providers to make better decisions.…”

Section: Introductionmentioning

confidence: 99%

Implementing Health Information System in King Hamad University Hospital Benefits and Challenges

Alseddiqi

AL-Mofleh

AlMannaei

et al. 2023

EJMED

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As a university hospital, it prides itself on using the latest techniques, medical and diagnostic curriculum, as well as up-to-the-minute medical equipment and fully equipped facilities. It has over 1700 employees and accommodates more than 311 beds and 9 operating theatres, a number of internal wards plus an intensive care unit with 12 beds. Healthcare organizations now believe in improved efficiency, reduced costs, improved patient care and quality of services, and safety when they are preparing to apply new information and communication technology (ICT) based applications. Health IT systems allow the gathering of data for use for quality management, outcome reporting, and public health disease surveillance and reporting. However, development is needed with all health IT, particularly concerning design, implementation, and integration between platforms within the surroundings of work.

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“…AI can also be used to identify high-risk behaviors and demographic areas where the frequency of illness is high. This information is used to develop targeted interventions and preventive measures to improve population health outcomes [8]. By enhancing patient outcomes, lowering costs, and allowing more individualized and effective care, the integration of AI and other cutting-edge digital technologies has the potential to completely transform the healthcare sector.…”

Section: Introductionmentioning

confidence: 99%

A Study on Scope of Artificial Intelligence in Diagnostic Medicine

Santhoshkumar¹,

Beaulah²,

Susithra³

2023

RRRJ

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Artificial Intelligence techniques are used to improve patient care and health systems. The use of machine learning and deep learning in healthcare is indeed prevalent, and it's fascinating to see how different medical data sources can be combined to diagnose diseases accurately. The variety of diseases that can be diagnosed using Artificial Intelligence (AI) techniques is impressive, and it’s interesting to note that different medical imaging datasets are used for feature extraction and classification to make predictions. This highlights the potential of AI in healthcare to assist healthcare providers in identifying diseases and providing appropriate treatments. It is also encouraging to see that AI can enhance the patient experience in hospitals and speed up rehabilitation at home. This can help to improve patient outcomes and reduce healthcare costs. Overall, it's evident that AI has the potential to revolutionize the healthcare industry and improve patient care. As the technology continues to evolve, it will be interesting to see how it is further applied in healthcare and the impact it has on patient outcomes. In this study, the scope of AI in diagnostic medicine has been analysed and summarized.

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A hybrid intelligent system for skin disease diagnosis

Cited by 22 publications

References 7 publications

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Implementing Health Information System in King Hamad University Hospital Benefits and Challenges

A Study on Scope of Artificial Intelligence in Diagnostic Medicine

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