Artificial Intelligence (AI) Innovations in Clinical Research
Author: Thalla Sanjeeva Reddy – Clinical SAS Programmer at Genpro
Majority of drugs take about 10 years or more to come to market, cost billions, and have a potential to even demolish an organization in certain cases where the late-stage trials fail after having poured in so much speculation. Also, Patients hardly can wait 15 years for a lifesaving drug. Thus, now is the time to focus on medication revelation and other productive process which can be easily achieved through Artificial Intelligence. AI can significantly reduce the time spent in the drug development process and can cut the expenses by more than half. In this blog, we aim to explain the different areas of a clinical task to which expert systems can be applied. This blog also states the various ways we may adopt Artificial Intelligence in the field of Medicine.
What is Artificial Intelligence (AI)?
From the very earliest moments within the history of the computer, scientists have dreamed of making an ‘electronic brain’. Of all the trendy technological quests, this search to make artificially intelligent (AI) computer systems has been one of the foremost ambitious.
It seems that very early on, scientists and doctors alike were captivated by the potential such a technology might have in Healthcare. Intelligent computers able to store and process enormous stores of knowledge, the hope was that they might become perfect ‘doctors in a box’, supporting or surpassing clinicians with tasks like diagnosis.
With such enthusiasms, a small but brilliant community of computer experts and medical professionals set about shaping a research program for a new discipline referred to as Artificial Intelligence in Medicine (AIM).
How can we apply AI to the field of Medicine?
AI programs have been developed and applied to analyze relationships between prevention or treatment techniques and patient outcomes. AI also applied to practices such as diagnosis processes, treatment protocol development, patient monitoring and drug development.
IBM’s Watson for Oncology is a solution that is fueled by information from relevant guidelines, best practices, and medical journals and textbooks. The system ev0061luates data from a patient’s health record, assesses medical evidence, and shows potential treatment options ranked by level of confidence, always providing supportive evidence. The oncologist can then apply their own expertise and insights to identify the most suitable treatment options.
What are many different types of clinical tasks to which AI can be applied?
Generating alerts and reminders: In actual situations, an expert system attached to a monitor can warn of changes in a patient’s state. In less severe conditions, it might scan laboratory test results or drug orders and send reminders or warnings through an e-mail system.
Diagnostic support: When a patient’s situation is critical, rare or the person making the diagnosis is simply inexpert, an expert system can help come up with likely diagnoses based on patient data.
Treatment assessing and planning: Systems can either look for variations, errors and omissions in an existing treatment plan, or can be used to formulate a treatment based upon a patient’s specific condition and accepted treatment guidelines.
Agents for information retrieval: Software ‘agents’ can be sent to search for and retrieve information, for example on the Internet, that is considered relevant to a problem. These agents encompass knowledge about its user’s preferences and needs and may also need to have medical knowledge to be able to evaluate the importance and utility of what it finds.
Image recognition and interpretation: Many medical images can now be automatically interpreted, from plane X-rays through to more complex images like angiograms, CT and MRI scans. This is of value in mass-screenings, for example, when the system can flag possibly abnormal images for detailed human attention.
Man-made brainpower (AI) innovations have made huge advances and a stream of companies are focusing, applying these advances to business challenges and, all the while, pioneering new trails. While numerous individuals might consider computerized reasoning as something recondite and in the domain of sci-fi, there are genuine applications that can offer organizations some assistance with solving complex issues, for example, understanding enormous information, increasing human choice making, or furnishing clients with master guidance.
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