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Use Cases of Artificial Intelligence and Machine Learning in Clinical

Development

Dr. Nancy Agens, Head,

Technical Operations, Pubrica

sales@pubrica.com

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.

Key words: Case Report Writing Services,

medical case study report writing, help in

case study writing, case report writing

help, case study report writing help,

writing a case report, how to write a

medical case report, best case report

writing service.

I. 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 case report writing service.

Here are few case studies about the AI

paved way to clinical development.

II. IMPORTANT CASES OF AI AND

MACHINE LEARNING

1. AI in cardiology

2. Practical implementation in

medicine

3. AI in global healthcare

4. Computer-aided diagnosis

5. A translational perspective of AI

and machine learning

1. AI in cardiology

AI provides all the necessary tools for

cardiologists. 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.

2. Practical implementations in

medicine

AI and clinicians work together to

formulate more précised 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. 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. The major concern is that

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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.

3. AI in global healthcare

Considering the benefits of AI

International Medical Device Regulators

Forum (IMDRF) 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.

4. Computer-aided diagnostics

As discussed earlier AI is used for

radiology detection. 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 (mainly in image

processing). The CAD also detects many

internal organs such as lungs liver, chest,

breats, etc by performing screening

examinations. It will be very usefulbfor 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.

5. A translational perspective of AI

and Machine learning For the past 30 years, there are no new

strategies used in the development of

drugs and medicines. This leads to some of

the medical errors causing adverse effects

to the patients, uncertain regulatory

clinical needs, delaying medical reports,

lack of information. If the entire process

ha changes to AI and machine learning or

anything related to computer vision, 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.

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.

III. CONCLUSION

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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.

REFERENCES

1. Johnson, K. W., Soto, J. T., Glicksberg, B. S.,

Shameer, K., Miotto, R., Ali, M., ... & Dudley, J. T.

(2018). Artificial intelligence in cardiology. Journal

of the American College of Cardiology, 71(23),

2668-2679.

2. He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., &

Zhang, K. (2019). The practical implementation of

artificial intelligence technologies in

medicine. Nature medicine, 25(1), 30-36.

3. Shiraishi, J., Li, Q., Appelbaum, D., & Doi, K.

(2011, November). Computer-aided diagnosis and

artificial intelligence in clinical imaging.

In Seminars in nuclear medicine (Vol. 41, No. 6, pp.

449-462). WB Saunders.

4. Shah, P., Kendall, F., Khozin, S., Goosen, R., Hu,

J., Laramie, J., ... & Schork, N. (2019). Artificial

intelligence and machine learning in clinical

development: a translational perspective. NPJ

digital medicine, 2(1), 1-5.