Artificial Intelligence (A.I.) and Medical Laboratory Technology .pptx

Artificial Intelligence (A.I.) and Medical Laboratory Technology

1. Department of Histopathology And Cytology Topic: Artificial Intelligence (A.I.) and Medical Laboratory Technology PRESENTED By: Sct . Aso , Dorcas Otei

2. OUTLINE Definition of terms Artificial intelligence Machine learning (ML0 Deep learning Natural learning processing (NLP) Medical laboratory technology 2. Some key roles of A.I. in medical laboratory technology Some roles of AI in histopathology and cytology laboratory. 3.Advantages of AI in medical laboratory technology 4.Disadvantages or challenges of AI in medical laboratory technology. 5.Possible Solutions to challenges of AI in medical laboratory technology. 6.Conclusion/recommendation

3. ARTIFICIAL INTELLIGENCE (AI) Artificial intelligence refers to computer systems capable of performing complex tasks that historically only a human could do such as reasoning, making decisions, or solving problems . AI is an umbrella term that encompasses a wide variety of technologies, including machine learning(ML), deep learning and natural language processing(NLP).

4. MACHINE LEARNING (ML) This is a common type of AI. ML is a subfield of AI that uses algorithms trained on data sets to create models that it's uses enables machine to perform tasks that would otherwise only be possible for humans, such as categorizing images, analyzing data, or predicting price fluctuation.

5. EXAMPLE AND USE CASES OF ML Recommendation engines. Speech recognition soft wares. A bank fraud detection service. Self-driving cars and driver assistance features.

6. DEEP LEARNING Deep learning is a method that trains computers to process information in a way that mimics human neural processes. Deep learning is a branch of ML that is made up of a neural network with three or more layers: Input Layers; Data enters through the input layers. Hidden Layers; Process and transport data to other layers. Output Layers; The final result or prediction are made in the output layers.

7. EXAMPLES OF DEEP LEARNING Self-driving cars. Chat bots. Facial recognition. Medical science. Speech recognition.

8. NATURAL LANGUAGE PROCESSING (NLP) NLP is a subset of AI, computer science and linguistics focused on making human communication, such as speech and text,, comprehensible to computers. NATURAL LANGUAGE TECHNIQUES Sentiment analysis. Summarization. Keyword extraction. Tokenization.

9. NLP BENEFITS The ability to analyze both structured and unstructured data such as speech, text messages and social media post. Improving customer satisfaction and experience by identifying insights using sentiment analysis. Reducing cost by employing NLP-enabled AI to perform specific tasks. Better understanding a target market or brand by conducting NLP analysis on relevant data like social media posts, focus group surveys and reviews.

10. NLP LIMITATIONS NLP can be used for a variety of applications but its far from perfect , many NLP tools struggle to interpret sarcasm, emotion, slang, context, errors and other types of ambiguous statements. Thus, NLP is mostly limited to unambiguous situations that don’t require a significant amount of interpretation .

11. EXAMPLES OF NLP Online chatbots. Text prediction and autocorrect.

12. MEDICAL LABORATORY TECHNOLOGY Medical technology has to do with the application of science to develop solutions to health problems or issues.

13. AN ILLUSTRATIVE IMAGE SHOWING A MEDICAL LABORATORY SCIENTIST USING A.I.

14. SOME KEY ROLES OF AI IN MEDICAL LABORATORY TECHNOLOGY Diagnostics and image analysis. Predictive analytics. Laboratory automation. Clinical decision support. Quality control and assurance. Data mining and research.

15. SOME ROLES OF AI IN HISTOPATHOLOGY AND CYTOLOGY LABORATORY. Image analysis and tissue classification. Cancer diagnosis and grading. Tumor margin assessment. Biomarker discovery Prognostic and predictive markers. Quality assurance and standardization. Research and education.

16. A deep learning algorithm trained to analyze images from MRI scans predicts the presence of a IDH1 gene mutation in brain tumors.

17. ADVANTAGES OF AI IN MEDICAL LABORATORY TECHNOLOGY Faster turnaround times. Increased accuracy. Reduced costs Higher value. Improved quality of care and the satisfaction of the patients by providing more personalized and precice diagnosis and treatment.

18. DISADVANTAGES OR CHALLENGES OF AI IN MEDICAL LABORATORY TECHNOLOGY High investment costs. Lack of proven clinical benefits. Privacy concerns. Number of decision makers. Ethical issues. Regulatory hurdles. Human factors. Technical limitations.

19. POSSIBLE SOLUTIONS TO CHALLENGES OF AI IN MEDICAL LABORATORY TECHNOLOGY. Education. Integration. Research. Collaboration. Standadization .

20. CONCLUSION AND RECOMMENDATION Conclusively, the role of AI cannot be overemphasized , thus I recommend AI to improve medical laboratory services.

21. THANK YOU .