TRINITY INSTITUTE OF MANAGEMENT AND TECHNOLOGY

Medical artificial intelligence (AI) primarily employs computer techniques to make clinical diagnoses and recommend treatments. When compared to sole human expertise, Artificial Intelligence (AI) predictive techniques enable auto diagnosis and reduce detection errors. AI-powered software can be trained to detect signs of a specific disease in the medical field.

AI can detect lung cancer diseases based on CT scans. Similar solutions exist that use AI to diagnose cancer by processing photos of skin lesions. Moreover, With AI networks’ ability to constantly train them, there is a good chance that their performance will improve significantly in the near future. Segmentation, detection, and classification, as well as quantification and grading, are common digital image processing tasks in diagnostic pathology.

This image shows the collaboration of the machinery technology and human expertise.

According to biomedical engineering that connects with computational monitoring heart rates, continuous measurements of bodily functions and metabolites, detection of stroke and other cardiac problems, classification of numerous pathological illnesses, including cancer, and prognosis of death in the clinic are all examples of current AI applications.

Current research is attempting to determine whether AI can aid in the prediction of behavioral changes associated with the onset of depression and other mental health issues.

1. Increase output.

The time-consuming processes are analyzed by image output through AI and it allows for the automation of manual tools. Because doctors have been thoroughly educated in their field and are up to date on current research, the use of AI greatly increases the likelihood of a faster outcome that can be matched with their clinical knowledge. Many people are concerned that artificial intelligence will eventually replace or reduce the need for human physicians, particularly in the clinical setting. Recent studies and data reveal, however, that rather than reducing clinician requirements, this tool is more likely to help and enhance clinical diagnosis and decision making.

2.   Improve the accuracy of diagnosis

Artificial intelligence technologies improve analytical precision in medical care. According to an experiment, an AI model trained to detect metastasized breast cancer tumors found 92.4 percent of them, compared to 73.2 percent for human diagnostics.

The most effective application of AI is when clinical diagnostics knowledge is combined with AI’s precision and efficiency. Hence, AI isn’t going to replace pathologists; it’s going to boost them.

3. Save money

AI-assisted diagnosis improves diagnostic accuracy by eliminating bias. Cases are analyzed by AI systems with 100 percent consistency. Due to higher precision, reducing diagnostic error and misdiagnosis while enhancing treatment accuracy with more thorough data would result in immediate cost savings.

4. Increase employee satisfaction

In medical laboratory sciences (MLS) deals with machinery techniques can achieve better workload distribution by spending less time on manual due to automated analyzers, repetitive duties, and more time evaluating unusual or complex cases that demand a higher degree of experience and skills.

5. Improve the outcomes of patients

AI aid improves diagnosis accuracy and consistency of analysis, which benefits not only the hospital in fact also concern with the medical profession. There are major improve outcomes of patients are as follows:-

The run continues AI-based software system for pathology in testing phase & professorship for Clinical AI.

• Use Language Your Patient Understands to Begin.
  • Assist patients and providers in establishing clear guidelines and expectations.
  • Identify and Collaborate with Patient Advocates
  • Don’t Stop Caring When You Leave the Hospital.
  • Encourage openness.

Asst. prof. HARJOT KAUR
(TIMT, Jalandhar)
Department of Medical lab sciences.