Many drugs take about 10 years or more to come on the market, cost billions, and have the potential to undermine the organization’s chances of failing late-grade tests and pouring out a lot of thought. Venture forward artificial intelligence (AI); a resilient mindset is very important in caring for these problems and sees it continuously as the future of successful drug development. With smart computers ready to store and process incomprehensible educational stores, the confidence was that they would be able to become ‘cradle experts’, assist or surpass doctors in their activities such as analysis.

To improve clinical trials, researchers in the f1ield of medicine are turning to artificial intelligence (AI). Encouraged by the rapidly growing numbers of medical data available to researchers, including those provided by electronic health records and wearable devices, high-tech technology has the potential to save billions of dollars, accelerate medical progress and increase access to diagnostic treatment.

Artificial intelligence is intelligence incontestable by machines, in contrast to the natural intelligence displayed by humans and animals, which involves consciousness and emotionalism. The excellence between the previous and therefore the latter categories is usually revealed by the acronym chosen.

Artificial intelligence in Health Care research:-

Artificial intelligence (AI) is increasingly being used in patient care to improve diagnosis and to find appropriate treatment options. Although there is a clear framework for the control of drug testing and medical devices, the majority of AI employed in health care is not regulated. AI is usually trained with a large amount of data, and its results are initially verified against that of decision-makers. 

Like new drugs or medical devices, an AI health system can have unknown benefits and lead to unexpected problems. However, there are no established clinical strategies for systematic testing of AI in health care. Big Data and Artificial Intelligence technologies are complimentary as Artificial intelligence can help to synthesize and analyse ever-expanding data.

Benefits of AI:-

  • Automation.
  • Smart Decision Making. 
  • Enhanced Customer Experience.
  • Medical Advances. 
  • Research and Data Analysis. 
  • Solving Complex Problems. 
  • Business Continuity. 
  • Managing Repetitive Tasks. v Minimizing business errors
  • Increased business efficacy.

Scope of AI in Clinical Research:-

v It helps indecisive the pattern of however well the drug is performing within the patients.

v It helps in decisive the pattern of aspect effects or adverse events that area unit seen in several patient pools. So this can cause efforts for avoiding such side effects at intervals with the patients.

v The post marketing study is also a large task as a result of it involves a vast variety of data that is tough for traditional eyes to analyse and predicts the defects in terms of effectualness and safety.

Advantages of Artificial Intelligence:-

The AI modification of clinical trials begins with the development of a follow-up rule, reducing or replacing test results that may be more responsive to change than traditional methods and using remote-connected technologies that reduce the need for long-distance patients to visit sites. Reliance on traditional site test results may lead to the development of sub-standard protocols that can reduce enrollment and lead to improper patient retention, driving trial costs or even program planning.

Incorporating AI into massive knowledge has the potential to make comprehension from real-world knowledge sizes (RWD) into protocol designs. Objective data from devices and sensors captured with real-time data from people as they go about their normal lives has the potential to obtain relevant clinical information and be used to evaluate and improve experimental objectives, storage facilities and procedures.

In the past, researchers relied heavily on oral or written evidence from patients during clinic visits and also directed clinical observations to monitor patient progress. This irrefutable evidence is unreliable, often inconsistent and internal and does not provide sufficient detail for analysis and decision making and while the reported patient outcomes are crucial to any trial, the addition of objective data to add context to independent assessment is particularly important.

Collecting real-time, real-world patient data on wearable devices, on the other hand, can help generate consistent evidence, targeting disease outbreaks and the effects of drug use on disease symptoms. Today there are many types of biometric signals that can be captured including heartbeat, blood pressure and sleep activity collected 24/7. It is much richer and more detailed than the data collected in the clinic and has the potential to respond better to change.

AI analysis of remote lives data and can detect where patients may be noncompliant, allowing clinical staff to intervene before patient data is extracted.

Ø AI drives down the time taken to perform a task. It permits multi-tasking and eases the work for existing resources.

Ø AI allows the execution of till now complicated tasks while not important price outlays.

Ø AI operates 24×7 while not interruption or breaks and has no period of time Ø AI augments the capabilities of differently abled individuals

Ø AI has mass market potential; it can be deployed across industries.

Ø AI facilitates decision-making by creating the method faster and smarter.

Artificial Intelligence (AI) and its Future:-

Artificial intelligence (AI) is increasingly being used in patient care to improve diagnosis and to find appropriate treatment options. Although there is a clear framework for the control of drug testing and medical devices, the majority of AI employed in health care is not regulated. The firmly controlled medicinal services industry has made little utilization of counterfeit consciousness in this way. One of the issues has dependably been that social insurance is excessively mind boggling. Keeping in mind the end goal to foresee anything around one’s wellbeing, we require data on demographics, proteins, multi-quality cooperation’s, ecological impacts, and an entire host of different features. Those conceivable outcomes are startling and energizing.

By Viharika A

Pharm D Karthikeyan A – Industrial Biotech

Leave a Reply

Your email address will not be published.