Artificial intelligence in healthcare: a review on predicting clinical needs

Houfani, Djihane; Slatnia, Sihem; Kazar, Okba; Saouli, Hamza; Merizig, Abdelhak

doi:10.1080/20479700.2021.1886478

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…(10) Was the exposure assessed more than once over time? (11) Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? (12) Were the outcome assessors blinded to the exposure status of participants?…”

Section: Methodological Quality Assessment Of the Studiesmentioning

confidence: 99%

“…Similarly, Ahmed et al [10] discussed aspects of precision medicine in their review, presenting works with different approaches to the use of ML in addition to discussing ethical aspects and the management of health resources. However, the work by Houfani et al [11] focused on the prediction of diagnoses, presenting an overview of the methods applied in the prediction of diseases.…”

Section: Introductionmentioning

confidence: 99%

“…However, the work by Houfani et al [ 11 ] focused on the prediction of diagnoses, presenting an overview of the methods applied in the prediction of diseases.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review

Cardozo¹,

Tirloni²,

Moro³

et al. 2022

JMIR Bioinform Biotech

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Background In recent decades, the use of artificial intelligence has been widely explored in health care. Similarly, the amount of data generated in the most varied medical processes has practically doubled every year, requiring new methods of analysis and treatment of these data. Mainly aimed at aiding in the diagnosis and prevention of diseases, this precision medicine has shown great potential in different medical disciplines. Laboratory tests, for example, almost always present their results separately as individual values. However, physicians need to analyze a set of results to propose a supposed diagnosis, which leads us to think that sets of laboratory tests may contain more information than those presented separately for each result. In this way, the processes of medical laboratories can be strongly affected by these techniques. Objective In this sense, we sought to identify scientific research that used laboratory tests and machine learning techniques to predict hidden information and diagnose diseases. Methods The methodology adopted used the population, intervention, comparison, and outcomes principle, searching the main engineering and health sciences databases. The search terms were defined based on the list of terms used in the Medical Subject Heading database. Data from this study were presented descriptively and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; 2020) statement flow diagram and the National Institutes of Health tool for quality assessment of articles. During the analysis, the inclusion and exclusion criteria were independently applied by 2 authors, with a third author being consulted in cases of disagreement. Results Following the defined requirements, 40 studies presenting good quality in the analysis process were selected and evaluated. We found that, in recent years, there has been a significant increase in the number of works that have used this methodology, mainly because of COVID-19. In general, the studies used machine learning classification models to predict new information, and the most used parameters were data from routine laboratory tests such as the complete blood count. Conclusions Finally, we conclude that laboratory tests, together with machine learning techniques, can predict new tests, thus helping the search for new diagnoses. This process has proved to be advantageous and innovative for medical laboratories. It is making it possible to discover hidden information and propose additional tests, reducing the number of false negatives and helping in the early discovery of unknown diseases.

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Section: Methodological Quality Assessment Of the Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review

Cardozo¹,

Tirloni²,

Moro³

et al. 2022

JMIR Bioinform Biotech

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“… Disease diagnosis artificial intelligence research has been in health care and medical diagnosis of diseases for a long time ago. Starting from expert systems in the 50 s of the last century, continuous efforts have been going on in this field until recently applying deep learning techniques for improved diagnosis of diseases [ 92 ]. Examples include lung cancer diagnosis based on CT scans and diagnosis of skin conditions through scanning skin images, which has recently been announced by Google [ 93 ].…”

Section: Wearable Devices and Machine Learningmentioning

confidence: 99%

Machine Learning for Healthcare Wearable Devices: The Big Picture

Sabry

Eltaras

Labda

et al. 2022

Journal of Healthcare Engineering

145

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Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases’ diagnosis, and it can play a great role in elderly care and patient’s health monitoring and diagnostics. With the great technological advances in medical sensors and miniaturization of electronic chips in the recent five years, more applications are being researched and developed for wearable devices. Despite the remarkable growth of using smart watches and other wearable devices, a few of these massive research efforts for machine learning applications have found their way to market. In this study, a review of the different areas of the recent machine learning research for healthcare wearable devices is presented. Different challenges facing machine learning applications on wearable devices are discussed. Potential solutions from the literature are presented, and areas open for improvement and further research are highlighted.

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“…Therefore, patients must create a schedule that includes organized visits to the dentist to detect any defects in the mouth early and improve their health condition. Modern technology and applications are of great significance in developing healthcare services, and among these technologies are artificial intelligence methods [6][7][8]. This term is mainly associated with robotics and describes how this technology is used and programmed to imitate the human mind and perform many tasks [9][10].…”

mentioning

confidence: 99%

Teeth and Technology: The Responsibility of Artificial Intelligence Techniques in the Dental Field- A Literature Review

M. Mijwil

2024

WJCMS

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With the significant growth of modern technology and its integration into many different industries, especially in the healthcare sector, artificial intelligence is one of the critical methods contributing to the development of medical fields, including dentistry. It possesses important and influential techniques that contribute to improving the results of patient care, diagnosis, treatment planning, and tracking the spread of diseases. These techniques play a major role in assisting dentists in diagnosing patients with high efficiency and accuracy. In this review, artificial intelligence techniques in developing the field of dentistry will be reviewed by highlighting the most important literature in which these techniques are involved. A search was conducted in Web of Science, Scopus, and PubMed databases from 2018 to 2023, where many articles were found (n=432), and articles that did not meet the selection criteria were excluded, resulting in thirty included. These articles involve artificial intelligence techniques in six areas: periodontal, dental implantology, forensic dentistry, oral medicine and pathology, orthodontics, and diagnostics/dentistry. In addition, this review presents matters related to artificial intelligence in dentistry, including data security, ethical concerns, and developing dentists' skills. This article finds that deep learning methods are widely utilized in the growth of dentistry, as the results show the accuracy of the results obtained, which is equivalent to the accuracy of professionals, and that it contributes to reducing human errors and revolutionizing the improvement of patient outcomes.

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Artificial intelligence in healthcare: a review on predicting clinical needs

Cited by 9 publications

References 25 publications

Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review

Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review

Machine Learning for Healthcare Wearable Devices: The Big Picture

Teeth and Technology: The Responsibility of Artificial Intelligence Techniques in the Dental Field- A Literature Review

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