Use cases of artificial intelligence and machine learning in clinical development - Pubrica
                     USE CASES OF 
AINRTTEIFLLICIGIAELNCE AND MACHINE  
LEARNING IN CLINICAL  
DEVELOPMENT
An Academic presentation by
Dr. Nancy Agens, Head, Technical Operations, 
Pubrica  Group: www.pubrica.com
Email: [email protected]
Today's Discussion
Outline
In Brief
Introduction
Important Cases of AI and Machine 
Learning  Conclusion
In Brief
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  
how to write a medical case report easily. Artificial intelligence is used  
worldwide for the development in their economy and to create a strong 
 base on their company standards.
Introduction
Artificial intelligence is ruling the digital world by creating 
 new standards in various fields.
AI has been creating a greater platform in the field of
healthcare development.
One of the most important accessibility of AI is to provide 
 information about medical case study report writing to  
make the data confidential.
On the other side machine learning enable the medicos 
 to come up with the best c ase report writing service.
Important 
 Cases of 
1. AI in cardiology
AI  and  
Machine  2. Practical implementation in medicine
Learning 3. AI in global healthcare
4. Computer-aided diagnosis
5. A translational perspective of AI and machine learning
Contd.
.
1. AI in
AI provides all the necessary tools for cardiologists.
Cardiology
AI was introduced to face the challenges of performing 
 real-world tasks by providing sociable algorithms.
It gives logistic regression which is useful to analyze  
statistical inference which delivers an algorithm about the 
 basic data, making it difficult for traditional statistical  
inference.
With this more appropriate data, cardiovascular 
medicine  is developed along with case writing 
services.
Contd.
.
2. Practical  AI and clinicians work together to formulate more
Implementations précised medicine.
 in Medicine
There are few challenges to develop a medicine 
 with this combination.
The very first issue is to collect a wide range of 
 data for processing an algorithm.
The collected data should be anonymized world- 
 wide and should provide sufficient information.
The current clinical unit doesn’t have this wide 
 range of data sharing.
Contd.
.
Following data collection, transparency is considered.
Transparency is done to obtain well-labeled algorithms.
Transparency is also an important factor in reinforcing discriminations.
This is mainly needed for physicians for the safety purpose of patients and it also 
 helps in writing a case report.
Along with that patient safety is another parameter in medicine implementation.
Contd.
.
The major concern is that patients should not suffer from the adverse effects of 
 using AI technologies.
The next big challenge is AI should provide standard data that transform all the 
 obtained data into useful data.
AI also assists in building workflow for many streams in the medical field.
However there might be some financial challenges in AI implementation in the
formulation of medicine, it gives an efficient product than the traditional 
methods.
Contd.
.
3. AI in 
Global  Considering the benefits of AI International Medical
Healthcare  Device Regulators Forum drafted a set of 
regulations
for the safety of people.
Many countries have changed their healthcare  
sectors towards AI and machine learning to develop 
 better standards in their companies.
The fastest transition to AI in companies will have a  
strong base on analysis, visual techniques, imaging 
 sources, etc.
Contd.
.
4. Computer-  
Aided  As discussed earlier AI is used for r adiology 
Diagnostic detection.
s Radiology detection can be achieved by computer- 
 aided diagnosis.
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 of data.
Contd.
.
The CAD also detects many internal organs such as lungs liver, chest, 
 breats, etc by performing screening examinations.
It will be very useful for the radilogists for clinical use and in case 
study
 report writing.
It is a belief that AI is going to be a major diagnostic tool in clinical 
 developmentent field.
The major AI sources will be computer tomography, Artificial Neural 
 network, Positron-emission tomography.
Contd.
.
5. Translational For the past 30 years, there are no new strategies used in 
 Perspective   the development of drugs and medicines.
of AI and  
Machine  This leads to some of the medical errors causing adverse 
Learning  effects to the patients, uncertain regulatory clinical needs, 
 delaying medical reports, lack of information.
If the entire process changes to AI and machine learning, 
 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 adverse events.
Contd.
.
Contd.
.
Some of the changes that took place were,
Machine learning determined drug discovery targets and molecular compounds. 
 Developing a pattern recognition for producing algorithms, available clinical
and imaging sets
To create a multimodel data which provides relevant pieces of information for 
 many particulars.
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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