Artificial Intelligence (AI) and Machine Learning are generating a great deal of creativity in healthcare precision medicine. The healthcare organizations are implementing precision medicine initiatives as practitioners begin to incorporate genomic and molecular data into their decisions on clinical treatment. Precision medicine is essential for the production of the drug, and the infrastructure needed to manage the vast amounts of data is vital to its success.

The idea of precision medicine continues to become a reality with the latest data collected from studies, CAR-T procedures, increasingly available genetic testing, and other applications. As this latest data-driven, customized treatment programs continue to be part of clinical practice in acute care settings such as oncology and mental illness, it is now time to examine the shortcomings in current IT technology systems in the broader clinical use of medicine.

There is no Precision Medicine without Artificial Intelligence!

Precision medicine is an emerging approach to treatment and disease prevention that takes into account the individual variation in biology, climate, and lifestyle for each person. This method helps clinicians and researchers to help assess what groups of people are trying to treat and prevent those diseases.

It requires a tremendous computing power (supercomputers), algorithms that can learn by themselves (deep learning) at an unparalleled rate, and an approach that uses the cognitive ability (AI) of newly developed doctors in general. Computer technology has become the frontline of countries that have shown their superiority in them. Deep learning algorithms for the diagnosis of cardiology, dermatology for oncology have been demonstrated at least as well. Nevertheless, the importance of integrating these algorithms with physician expertise needs to be emphasized.

AI is divided broadly into three steps: Artificial Narrow Intelligence (ANI), Artificial General Information, and Artificial Super Intelligence; ANI will appear the most often over the next decade. ANI will evaluate data sets, draw conclusions, understand and endorse doctors’ work.

Precision Medicine Applications: Current precision medicine approaches include statistical phenotyping techniques, innovative drug development, and production models, and applications to match patients with effective therapies and clinical trials, as well as AI and machine learning. The pharmacogenomics precision medicine system is one of the largest, and more than 230 recruitment drugs have now been made available on the tag with genetic guidance.

The Use of the Cloud Made AI and Machine Learning Easier: Cloud computing is capable of processing data more rapidly, and the ability of these machine learning and artificial intelligence systems is required if trillions of data points are used. Although the cloud is convincing, teamwork, and shared expertise will make the precision medicine system even more convenient for accessing and making insights into the data workable. Machine learning and other aspects of AL have tremendous potential for finding new ideas.

Future of Precision Medicine & Challenges Ahead: In addition to health IT challenges, payment models need to be built to suit the business case in which services for smaller populations and broader trends toward health care value payments are changed to provide clinically useful precise medicine data. The transition would have consequences for the pharmaceutical, payers, and manufacturers sectors.


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