Use cases of artificial intelligence and machine learning in clinical development – Pubrica

1. USE CASES OFARTIFICIAL INTELLIGENCE ANDMACHINE LEARNING IN CLINICAL DEVELOPMENT An Academic presentationby Dr.NancyAgens,Head,TechnicalOperations,Pubrica Group: www.pubrica.com Email:sales@pubrica.com

2. Today'sDiscussion Outline InBrief Introduction Important Cases of AI and Machine Learning Conclusion

3. InBrief Artificial intelligence, machine learning will create a greater platform for clinical development in the future. The AI tools will be more beneficial than the traditional methods for detection and to determine howto write a medical case reporteasily. Artificial intelligence is used worldwide for the development in their economy and to create astrong base on their companystandards.

4. Artificial intelligence is ruling the digital world bycreating new standards in variousfields. AI has been creating a greater platform in the fieldof healthcare development. One of the most important accessibility of AI is toprovide information aboutmedical case study report writing to make the dataconfidential. On the other side machine learning enable themedicos to come up with the best case report writingservice. Introduction

5. Important Cases ofAI and Machine Learning 1. AI incardiology 2. Practical implementation inmedicine 3. AI in globalhealthcare Computer-aideddiagnosis A translational perspective of AI and machinelearning Contd..

6. AI provides all the necessary tools forcardiologists. 1. AIin Cardiology AI was introduced to face the challenges ofperforming real-world tasks by providing sociablealgorithms. It gives logistic regression which is useful to analyze statistical inference which delivers an algorithm aboutthe basic data, making it difficult for traditional statistical inference. With this more appropriate data,cardiovascularmedicine is developed along with case writingservices. Contd..

7. AI and clinicians work together to formulatemore précised medicine. 2. Practical Implementations inMedicine There are few challenges to develop amedicine with thiscombination. The very first issue is to collect a wide rangeof data for processing analgorithm. The collected datashould be anonymizedworld- wide and should provide sufficientinformation. The current clinical unit doesn’t have thiswide range of datasharing. Contd..

8. Following data collection, transparency isconsidered. Transparency is done to obtain well-labeledalgorithms. Transparency is also an important factor in reinforcingdiscriminations. This is mainly needed for physicians for the safety purpose of patients and it also helpsinwritinga casereport. Along with that patient safety is another parameter in medicineimplementation. Contd..

9. The major concern is that patients should not suffer from the adverse effectsof usingAI technologies. The next big challenge is AI should provide standard data that transform all the obtained data into usefuldata. AI also assists in building workflow for many streams in the medicalfield. However there might be some financial challenges in AI implementation inthe formulation of medicine, it gives an efficient product than the traditionalmethods. Contd..

10. Considering the benefits of AI InternationalMedical Device Regulators Forumdrafted a set ofregulations for the safety ofpeople. 3. AI inGlobal Healthcare Many countries have changed their healthcare sectors towards AI and machine learning todevelop better standards in theircompanies. The fastest transition to AI in companies will have a strong base on analysis, visual techniques,imaging sources,etc. Contd..

11. 4. Computer- Aided Diagnostics As discussed earlier AI is used for radiologydetection. Radiology detection can be achieved by computer- aideddiagnosis. ANN is a tool developed by artificial intelligence which is used to detect breast cancer in the form of mammograms. ANN is the algorithmic representation ofdata. Contd..

12. The CAD also detects many internal organs such as lungs liver, chest, breats, etc by performing screeningexaminations. It will be very useful for the radilogists for clinical use and in casestudy reportwriting. It is a belief that AI is going to be a major diagnostic tool in clinical developmententfield. The major AI sources will be computer tomography, Artificial Neural network, Positron-emissiontomography. Contd..

13. For the past 30 years, there are no new strategies usedin the development of drugs andmedicines. 5.Translational Perspective of AI and Machine Learning This leads to some of the medical errors causingadverse effects to the patients, uncertain regulatory clinical needs, delaying medical reports, lack ofinformation. If the entire process changes to AI andmachinelearning, there will be a greater platform towards much effective growth in innovative techniques in clinical development with an abrupt drug, standardized therapies, improved safety, reducing adverseevents. Contd..

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15. Some of the changes that took placewere, Machine learning determined drug discovery targets and molecularcompounds. Developing a pattern recognition for producing algorithms, availableclinical and imagingsets To create a multimodel data which provides relevant pieces of informationfor manyparticulars.

16. Conclusion However AI, Machine learning have subsequently shown growth in the clinical development fields, it is predicted that it will create a benchmark in many companies using artificial intelligence for their research purposes.

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