Abstract

Editorial

Artificial Intelligence (AI) in the Contexts of Healthcare Transformation

Many companies are currently, using AI for lots of scenarios including improving clinical workflows, managing claims, detecting fraud, as well as predicting hospital-acquired infections. The healthcare industries are witnessing rapid expansions with large volumes of data and growing difficulties in patient outcomes and cost. Early accommodating of AI in the health services setting are harvesting the benefits in terms of adding to their bottom line results and patient care, the open-source and automation leader in AI(, is strengthening guiding healthcare organizations to deliver solutions by AI applications, that keep changing the industry profile and outcomes [1].

Vastly Growing Application (AI) at Heart of Health Industry

Updated evidence concerning the rhythm of which (AI) has become an unavoidable element of nowadays technology shall be either, deeply troubling or highly encouraging, based on integrating data-rich algorithms to the complex personal practice of patient care. Difficulties are so far common over utilizing AI elements; as the trust of clinicians begin to operate trading deployments through medical management support systems, Diagnostic means of controlled trials, and operation flow of maximum aids. Similar offerings will still have impacts, especially on trained groups, whom always questioning the relevant accuracy and possible errors of the information where such algorithms already trained. Moreover, many things are revealing significantly astontioning outcomes that are making providers adhering to shorter access. It appears obviously that nothing will stay same, as soon as the industry of healthcare engaged in Artificial intelligence operations, regardless of where any institutions or individual falls along the enthusiasm spectrum – and such moment may be a reality quite soon. “It has been concluded that an ever-increasing number of cases that have the potential to truly transform the way providers care for patients, “will be seen soon. There are great deals of activities around the innovation platform of open-source health, which is focusing on making (health services cheaper and more efficient to build, validate secure, share, deploy, and eventually applications networking over institutions [2-4].”

Last Mile in Process Transformation

“At current times, focusing on transformation enabled by the last-mile problem associated with the Medical process requires to get broadly rolling out of algorithmic-enabled care, in healthcare. Game-changing innovations and amazing number of potentials were displayed at Artificial Intelligence at (WMIF) World Medical Innovation Forum. From in-vitro fertilization simplifying to personalizing cancer care and interpreting lab tests, through monitoring surgical video in real-time or aiding in the diagnosis of pneumothorax. The creativity and integrity of evidence in the flourishing Artificial intelligence research community are both encouraging and staggering. “There are true –increase in the number of cases that have the potential to actually transform the way provider’s services for patients. Any of such solid solutions for particular use cases is potentially saving tenths, hundreds, or thousands of lives by time if implemented appropriately and brought to scale. Likewise, achievements shall for sure be nothing to sneeze at. However, (AI) potentially provide the healthcare system privileges that cannot be estimated on an individual scale. Deep learning, neural networks, machine learning, natural language processing, and all of the other elements of the Artificial intelligence ecosystem are poised to bring about a whole revert in the Domain , from deliver care instead of how doctors are trained to make powerful decisions[5-7].

What Opportunities & Challenges of Bringing AI to Healthcare?

One of the major challenges, in applying Artificial intelligence is how the giants of consumer technology e.g. Amazon, Uber, Apple, and Google integrate artificial intelligence scenarios in their search functions, apps smartphones, and [8]. Error in utilizing complex data of software and complex algorithms to study human cognition may still difficult ... variety of ethical implications around the use of AI in healthcare. Smart machines using is crucial to make or assist with them address issues of permission, and privacy, accountability, transparency.

Implementing Examples

Radiology

Imaging results interpretation is essential to help physicians in identifying minor changes in an image that a clinician might accidentally miss. The technology can achieve improvements in certain statistical metrics in isolated cases, as opposed to specialists.

Diagnosis of Diseases

There are many techniques like neural networks, Support vector machines, Decision trees, and many more. Each of these techniques is described as having a “training goal” so “classifications agree with the outcomes as much as possible, which can be applied in cardiovascular diseases and diabetes mellitus case.

EMR (Electronic Health Records)

Accuracy achieved by 70–72% in predicting individualized treatment response at baseline. Using an Artificial Intelligence tool, which scans EHR data.

Drug interaction

In order to standardize the measurement of the effectiveness of their algorithms, Researchers continue to use this corpus.

Conclusion

As artificial intelligence is going to continue to make faster and precision-driven clinical processes more informed, the healthcare practitioners and the doctors of the future years shall be more equipped to use artificial intelligence blinked skills and new for care process and treatment and. This will ultimately push forward the healthcare standard and management to a completely new era.

Acknowledgement

None.

Conflict of Interest

No conflict of interest.

References

1. Artificial Intelligence Promises a New Paradigm for Healthcare.
2. Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, et al. (2017) Chex Net: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. ArXiv: 1711.05225 [cs.CV].
3. (2016) Artificial intelligence (AI) Algorithms Need Managers, Too. Harvard Business Review.
4. Coiera E (1997) Guide to medical informatics, the Internet and telemedicine. Chapman & Hall, Ltd. UK.
5. Power B (2015) Artificial Intelligence Is Almost Ready for Business. Massachusetts General Hospital, USA.
6. Bahl M, Barzilay R, Yedidia AB, Locascio NJ, Yu L, et al. (2018). High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision. Radiology 286 (3): 810-818.
7. Ab Bloch Budzier S (2016) NHS using Google technology to treat patients.
8. Lorenzetti L (2016) Here is how IBM Watson Health is transforming the Health Care Industry.

Abstract

Many companies are currently, using AI for lots of scenarios including improving clinical workflows, managing claims, detecting fraud, as well as predicting hospital-acquired infections. The healthcare industries are witnessing rapid expansions with large volumes of data and growing difficulties in patient outcomes and cost. Early accommodating of AI in the health services setting are harvesting the benefits in terms of adding to their bottom line results and patient care, the open-source and automation leader in AI(, is strengthening guiding healthcare organizations to deliver solutions by AI applications, that keep changing the industry profile and outcomes [1].