As the medical sciences continue to expand their avenues and find the cures and drugs for diseases and implement new technologies in their studies, the positive impact of the same is being noticed. Artificial Intelligence is being implemented nowadays to accomplish various tasks in the medical sector as this kind of help in generating conclusions based on concrete data and evidence easily which facilitates further work and procedures to go smoothly.
Understanding Artificial Intelligence (AI)
Artificial Intelligence is the intelligence demonstrated by the machines and algorithms which are tech-based and machine-based, this is nothing like human wisdom or emotions but they are more of the human problem-solving ability using algorithms.
Such machines and systems are significantly intelligent due to the advanced analytics systems like machine learning and deep learning which enable the process of automation in which the artificially intelligent machines continue to perform their tasks by getting the fuel from unstructured data, images, and texts in the system inputs.
Based on the strength and levels of artificial intelligence the functions can include single and simple tasks for some and more complex and humanlike tasks for other kinds of intelligence systems.
What are Clinical Trials?
Research and invention play a crucial role in the dynamics of any field. However, in medical sciences, such researches and trials take place in order to move ahead with the new drugs, treatments, and solutions for the pre-existing and ever-evolving diseases.
There are two kinds of research that take place in observational studies and clinical trials.
In observational studies, volunteers for the medical causes are observed using methods like medical tests, exams, questionnaires about the various issues to have an insight into the lifestyle of such adults and cognitive health.
However, in clinical trials, the research takes place to have an idea of the help medically which is possible in the detected diseases or cases. They form the conclusions which allow the researchers to find out the possibilities of a new treatment, drugs, and devices that can be of some use in the ongoing health condition.
Hence it will not be wrong to say that observational studies form the basis of the clinical trials.
Phases of Clinical Trials
Clinical Trials are a long and tedious process both in action and time duration needed. It can be understood as once the testing of a certain drug or medication is started there can study about the positive and negative implications of the same but in medical sciences, there is a fair chance of long-term side effects which might grow to develop its symptoms in the long run.
The drugs or the devices which might seem well theoretically and based on the research conclusions might not always be of the same practical advantage as there has to be regarded about the age group, lifestyle, and even prevalent healthcare conditions.
Keeping in mind all such things whatever is discovered cannot be immediately put to action on a large number of the population, Hence there are phases of trials before the medicinal usage gets approved for usage.
Phase 1: This is done in a group of 20-80 people to determine the correct dosage of the drugs and their general and potential side effects.
Phase 2: In this stage, the treatment of the drug is tested in a comparatively larger number of people, around 100-300 for further evaluation. In this, the prevailing health conditions of people is considered and this primarily focuses on the effectiveness of the drugs as well. This phase can last up to several years.
Phase 3: This is performed on around 1000-3000 people and works on understanding the side effects, effectiveness based upon age and dosage, consequences with the combination with other drugs that the subject might be consuming. If the trial results come out to be positive the drug gets approved by the FDA.
Phase 4: These trials take place after the approval of the drug. The effectiveness of the device or the medicine hence approved is monitored as it gets used in the larger population. This sometimes proves to be of great insight as some conclusions take form when the drug gets ample usage.
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Applications of AI in Clinical Trials
For the medical industry, artificial intelligence can be applied in many stages ranging from identification of drug tests to repurposing of old drugs. Not just this AI is being of great help in improving the usage of the data to find suitable patients and even keep a track of the journey of the patients and drug success throughout the clinical trials.
Let's have a look at how AI is being implemented:
1. Designing Clinical Trials
This is playing a crucial role in changing the dynamics of the traditional trials which were done with limited and available data. Artificial intelligence is capable of fetching and utilizing the available data from various sources.
The scientific and research data of the past trials, patient support programs, case studies of special medical situations, etc have refueled the trial designs. AI-enabled technology and systems have the committed ability to collect, organize and analyze the data generated by the past trials irrespective of their success or failure rate.
Such algorithms are able to find the fittest scenarios and timings of the trials. Auch intelligent systems can also help in overcoming the drawbacks of past trials.
2. Improved Patient Selection
While rummaging through the database made available AI-based algorithms can improve patient selection and can improve the effectiveness of the trial. This can be done by interpreting various data sources made available related to the electronic health records, medical imaging, and even omics data can be used and analyzed by artificial intelligence systems before determining a person suitable for clinical trials.
Also, artificial intelligent-based study systems can use their algorithms to find an ideal set of people to perform trials on. That helps in reducing population heterogeneity which is essential to reduce variability beforehand and study power can be increased.
AI systems are capable of doing prognostic enrichment to select patients with measurable clinical endpoints alongside predictive enrichment which means identifying the patients most likely to respond to the treatment so that only the most adequate patients take part in the trials as per the criteria.
AI Applications in Clinical trials
3. Site Selection
Based on the geographical and climatic conditions of the area the artificially intelligent programs can go on to suggest suitable sites and locations for the testing.
Site with an adequate number of professionals to attend the patients, proper resources availability, and emergency facilities are preferred and advanced analytics systems can integrate themselves with systems like maps and population count data to provide best solutions for a proper site for performing clinical tests by just a search post activation.
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4. Patient Monitoring
AI Algorithms can make the best use of the data by keeping a track of the pre-trial condition and post-trial symptoms of the patient. By using sensors-based wearable devices while performing the trials the real-time safety monitoring and effect monitoring can be done with the correct evaluation of the risks.
Also, all of this can be recorded automatically by the AI-based system connected to the devices whenever they detect a change in pulse or body temperature, etc. continuously throughout the trial period and in a few hours after the drug is given. Such data recorded can be transferred across the system in no time in the case of further research and analysis with lesser chances of discrepancy.
5. Fostering More Trials
Huge numbers of conclusions and data is drawn by the trials of medicines and such data is not always collected in a way that it can be safe to share it with other companies whenever they go on to perform the same procedures.
Artificial Intelligence functions on data when it comes to procedures like these. In such a case artificial intelligence can allow the companies to share the data with other companies to help them get insights about the various criteria that might follow in their research and to some extent can also improve and present the data due to a self-learning intelligent system in the best way so that it can be used by other companies in collaboration and can help in creating cumulative data for separate and similar clinical testing measures which can turn out to be more comprehensive and better.
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Clinical Trials are more of a research dependent procedure that generates more and more data. In general, this is a long and time-consuming process with no subtle alternative. Artificial Intelligence can however increase its efficiency by providing suitable options and improvisations by analyzing and concluding data.
Like choosing the apt patients without compromising on their health-related data, monitoring the real-time updates with the help of other specific devices, having a track record of the usage of the drug or device can be done in a rather convenient way using artificial intelligence.