The year 2024 is predicted to be a disruptive year when it comes to artificial intelligence (AI) and machine learning. AI and machine learning specialists have been working to integrate efficient AI and machine learning algorithms into industries. The Healthcare industry is known to be at the forefront of any country’s global economy, and since the pandemic, healthcare concerns have been growing among people.
If we particularly talk about the Healthcare industry, multiple subfields are crucial to be looked after seamlessly. It is important to ensure effective and smooth healthcare operations, such as drug discovery, medical imaging, and other several clinical trials. In this regard, scientists and developers have collaborated to integrate AI and machine learning in healthcare, which may potentially positively affect the healthcare industry.
Transforming Clinical Trials with AI and Machine Learning:
AI and Machine Learning have great potential to enhance research operations, clinical trial designs, and overall management outcomes. Using past clinical trial data, algorithms can discover areas for protocol optimization, such as proper endpoint selection, sample numbers, and study lengths.
The result? more efficient and informative studies, which reduce costs, shorten timeframes and ultimately increase the chance of successful outcomes. Furthermore, the capacity to interpret complicated data allows researchers to design studies that reduce patient burden while increasing participant retention.
How do AI and Machine Learning help in Clinical Trials?
Study Design:
Automating the build stage can play a crucial role in designing case report forms (CRFs) and building databases. In older times, data managers and administrators generate a large number of CRFs manually, which turns out to be time-consuming and sometimes causes human errors. Now, in 2024, AI and machine learning have become a part of the healthcare industry.
If you ask yourself “How does AI reduce human error?”, Well, there are several methods and ways that AI mitigates the risk of human error, but in healthcare, since AI and Machine Learning have been integrated, the Study design phase in clinical trials have enhanced and outcomes have been generated more efficiently.
Patient Recruitment and Management:
Patient recruiting is usually the most time-consuming part of a research experiment. Data managers must locate and identify a group of subjects, collect data, and apply inclusion/exclusion criteria to filter and select participants. High-quality patient recruitment is critical to a trial's success.
AI and machine learning algorithms may be applied to patient recruitment and management to improve efficiency, quality, and retention. These algorithms can collect subject information while screening and filtering potential participants. They can use data sources like medical records and social media information to identify subgroups and regions that may be relevant to the experiment. AI can also inform medical personnel and patients about clinical trial prospects.
Electronic Health Records (EHRs):
AI and machine learning specialists suggest that integrating AI and machine learning into clinical trials can play a significant role in enhancing patient data management. Since the answer to what is natural language processing has become well-known, scientists have found ways to integrate NLP into healthcare.
When it comes to managing EHRs, healthcare specialists say that it becomes easy and less time-consuming to match patient’s data and extract relevant information from an unstructured EHR with the help of AI and machine learning, and since NLP is being used, the process becomes more efficient and ensures accuracy in the outcome.
Real-Life Application of AI and Machine Learning in Healthcare:
Feasibility Assessment:
A few years back, the processes and methodologies used for feasibility analysis were time-consuming, required a lot of resources, and were prone to human errors. In 2024, by implementing these algorithms, clinical research procedures have resulted in becoming less time-consuming, enhanced accuracy, and streamlined data collection and management.
Patient Enrolment and Recruitment Processes:
The patient recruitment and enrolment process is a very crucial phase of every clinical trial. Managing patient data efficiently is important to record every patient’s medical history and other health-related information. Integrating AI and Machine learning algorithms enhances the management of Electronic Health Records (EHRs), streamlines data collection, and reduces the time taken in that process.
Clinical Research:
Research and studies in healthcare are time-consuming and sometimes quite expensive. Accessing research articles, and experimenting with procedures and medicines using various equipment and machinery is expensive. Sometimes, any failure in such machines causes, and repair is costly. Using AI and Machine learning in clinical research reduces the cost of buying multiple large-scale equipment, and improves the outcome of research and studies.
Final Thoughts:
The pharmaceutical sector is undergoing a significant transition as a result of the use of AI and Machine learning in clinical research. Early adoption of this technology and the numerous studies published in the pharmaceutical industry highlight how important these technologies will be in transforming clinical research in the future.
Pharmaceutical businesses can speed up drug discovery, improve patient outcomes, streamline clinical trials, and usher in a new era of healthcare innovation by utilizing AI and Machine Learning. Clinical research's incorporation of rapidly developing technology portends an infinite future for the field, driving it toward ground-breaking discoveries and a more effective, patient-centered approach to clinical trials and drug development.