Abstract:
Artificial intelligence is the science of big data analytics and information system. During the last decade it has attained significant attention in all the growing fields including therapeutics and theranostics. The current study is the retrospective research that encompass the development and advancement in the diagnostic sciences by using artificial intelligence in diagnosing various ailments. The use of deep learning, machine learning and big data algorithms for diagnostic procedure has shown promising results. Accurate and timely diagnosis ensures better prognosis of diseases. The vast amount if diagnostic data, lab reports and imaging scans can be accurately analyzed by using AI algorithms thus providing early diagnosis of serious medical ailments. These techniques can also be helpful in identifying the otherwise complicated pathogenic mechanism and differentiate contrasting disease from one another so that diagnostic rule out becomes easier and faster. Medical imaging analysis stands out as a primary application of AI in diagnostic sciences. By leveraging AI algorithms, radiologists can efficiently analyze radiological images, including X-rays, CT scans, and MRIs, leading to improved accuracy in detecting anomalies and making diagnoses. This technology has the potential to expedite the diagnostic process, facilitating earlier disease detection and enhancing patient outcomes. Furthermore, AI shows promise in the field of pathology. Pathologists can benefit from AI algorithms that analyze digital pathology images, assisting in identifying cancerous cells, determining tumor margins, and predicting prognosis. This technology not only enhances accuracy but also streamlines workflow in pathology laboratories, potentially reducing turnaround times and increasing overall efficiency. Beyond image analysis, AI can play a crucial role in clinical decisionmaking. By integrating patient-specific data, such as medical history, genetic information, and treatment outcomes, AI algorithms can generate personalized diagnostic and treatment recommendations. This supports healthcare providers in making evidence-based decisions, resulting in improved patient care. Nevertheless, the integration of AI in diagnostic sciences presents challenges that must be addressed. Ensuring the accuracy, reliability, and ethical use of AI algorithms is paramount. Collaborating effectively between AI systems and healthcare professionals is crucial to optimize the diagnostic process and ensure patient safety. In conclusion, AI holds immense potential for transforming the field of diagnostic sciences. From enhancing medical imaging analysis to aiding pathology and clinical decision support, AI technologies can significantly improve diagnostic accuracy, efficiency, and patient outcomes. Addressing challenges and harnessing the capabilities of AI will be crucial in leveraging its full potential to revolutionize healthcare delivery.
Page(s):
224-224
DOI:
DOI not available
Published:
Journal: Abstract Book on International Conference on Food and Applied Sciences (ICFAS-23) 3-5 August 23, Volume: 0, Issue: 0, Year: 2023