Artificial intelligence uses in clinical and laboratory diagnosis

Artificial intelligence uses in clinical and laboratory diagnosis

Review article

Maha M. El Alem

Chief Researcher of Pathology

Animal Health Research Institute (AHRI) ( Zagazig branch),

Agriculture Research Center (ARC), Egypt

ABSTRACT

 The clinical applications of medical artificial intelligence (AI) aids in  diagnosis which playing important role in  treatment protocols and decision-making, so that  accelerate the  system of health care  via introducing    more information accompanied by available health data.

Artificial intelligence technologies include Varity of  machine learning approaches with the structural data, for example traditional support vector machines as well as neural networks, plus advanced deep learning techniques. As well as, they provide unstructured data for the natural language processing. Recently, AI applications playing important role in veterinary medicine, in addition to broader industry valuable tool.  

AI recordred its value in accelerate the accuracy ai addition to efficiency of detecting and diagnosing animal diseases, moreover their treatment. This technology considered as a very good support system in collaboration with veterinarians. Moreover, innovation in healthcare system acceleration.

AI accompanied with everyday life plus medical practices via advancements as excellent health monitors moreover diagnostic algorithms.

Keywords: artificial intelligence, laboratory medicine, healthcare, clinical diagnosis, support system and veterinary AI.

INTRODUCTION

Artificial intelligence (AI) is nowadays playing an important role in clinical diagnosis and treatment. The progress of medical AI includes programs performed to help clinicians in diagnoses process, selection of proper therapies and predicting outcomes of patient. These systems aid technologies such as artificial neural networks (ANNs), fuzzy expert systems, hybrid intelligent systems, moreover evolutionary algorithms. Advances in excellent medical technologies are made the way for the emergence of medicine new fields.

By 2021, the AI application in the sector of healthcare considered fully operational.  AI opportunity in veterinary medicine is expected to grow rapidly and supporting many applications as imaging, prediction of diseases, treatment of cancer and radiology (Rahul, 2023).

Recently, many devices implemented in laser-based technology playing an important role in screening of skin in addition to veins.

These devices dealing with tasks simplyand locating veins for drawingblood or placingIVs,improving the medical professionals and patients alike process. AI's involvement in diagnosis of medical dates from 1970s within the MYCIN at Stanford University, a system performed to identify bacterial infection development.

Nowadays, artificial intelligence act via gathering data; interpreting it; learning from patterns and delivering the intended outcomes (Davenport and Kalakota, 2017).

Applications of AI in Veterinary Practice

The National Animal Disease Referral Expert System (NADRES), made by ICAR NIVEDI, act as a synchronized platform aids in alarm mechanisms and rapid actions for stakeholders. Its firstly focus lies in the prediction; prevention and management outbreaks of animal diseases, involve zoonotic threats via leveraging exchange of data , epidemiological analysis and adaptive strategies when required.

 AI   integration   and advances play a critical role in   animal diseases addressing presents transformative opportunities for preempt the outbreaks plus the animal health sector enhancement (Savleen et al., 2022).

Artificial intelligence play excellent role in clinical settings; such as   left atrial enlargement detection in dog exposed to thoracic radiographs (Li et al., 2020).

 Clinical microbiology is improved AI techniques. Moreover; genomic data from bacterial isolation, outcomes the analysis of  metagenomic on original samples in addition to mass spectrometry data obtained from the isolated bacteria as well as the high-resolution digital images are some of the substantial datasets being utilized to construct AI-based diagnostic system (Peffer-Smadja et al., 2020).

 The microscopic traditional techniques, represented in Gram staining, ova and detection of parasite, moreover, the histopathological analysis, play significant advancements via machine-based image processing. Neural networks play excellent role in differentiating Gram-positive or Gram-negative bacteria and in identifying cocci or bacilli via blood culture samples (Smith et al., 2018).

 The applications of Computer vision have provide excellent role  in protozoa in fecal samples stained with trichrome identification with providing validated programs help accuracy and recognition limitations (Mathison et al., 2020).

 Machine learning systematic studies aid in antibiotic identification with susceptibility and resistance profiles. Moreover; the prevalent machine learning approaches supported vector machines;genetic algorithms; artificial neural networks moreover fast classifiers (Weis et al., 2020).

Animal welfare assessing is important for maintaining health insurance; productivity; recognition of injuries or stress factors and prevention of declines in well-being. This measure has considered essential as a marketing strategy for promoting humane practices in animal treatment. Moreover, many visual professional or veterinarians inspections could be subjective and resource-intensive via training requirements. Advancements in remote sensing, computer vision and AI considered innovative technologies for biometric monitoring of cattle behaviors as chewing behavior for extraction  the critical physiological parameters tied of welfare (Sigfredo et al., 2022).

As well as, AI systems incorporating machine learning and deep learning models have been involved in prediction analysis.

These systems are utilized for monitoring, physiological changes in animals for early detection of infections, stress factors, diseases, or parasites (Neethirajan et al., 2017; Neethirajan and Kemp, 2021).

 Sensor technology is recently used in livestock management. For instance, electronic collars and ear-tag sensors for sheep offer reliable data collection with accuracy exceeding 90% for specific operational parameters (Mansbridge et al., 2018).

 Predictive protocol leveraging historical data have also been explored to evaluate traits like carcass quality from an early age (Shahinfar et al., 2019).

CONCLUSION:

In recent decades, technological innovation in healthcare has accelerated. The use of AI offers solutions to many animal-related issues. Furthermore, AI-driven algorithms developed by veterinarians play a crucial role in diagnosing diseases.