Sets of mathematical algorithms that define the relationships between variables – are the foundation of machine learning techniques. Computation improves various critical areas of clinical research in general, and AI-based methods offer even more uses for researchers. Despite not being widely used yet, machine-learning algorithms are already influencing various fields of clinical research, such as recognizing the value of massive data.
The applications of machine learning are as follows:
- Personalized medicine
Personalized treatments are more successful when individual health combine with predictive analytics. It is also grown for additional research and better disease assessment. Currently, physicians are confined to choosing from a limited number of diagnoses or estimating the patient’s risk based on his clinical history and accessible genetic information.
However, machine learning in medicine is making significant progress. In the next few years, the biosensor and devices based on Machine learning will form with sophisticated health measurement capacities.
- Reconstructing disease
Clinical researchers may “reconstruct the basic mechanisms of disease” by combining machine learning with multimodal datasets and nearly unlimited computing power, according to Colin Hill, CEO and co-founder of GNS Healthcare. GNS Healthcare’s AI-powered simulation platform Gemini, for example, provides a computer model of the course of multiple myeloma and treatment responses.
This model “harnesses the power of causal machine learning and simulation and in-depth clinical and genomic patient data, to enable pharma companies to simulate drug response at the individual patient level.
- Hypothesis testing
In this field of medical research, predicting the results for a particular scenario is no easy task. By combining information from clinicians and data-science tools, including machine learning, the researcher can develop a hypothesis, model it and iteratively replicate the process.
- Recruiting patients
In clinical research recruiting patients is challenging. In this challenge, machine learning can help the researcher.
- Big data
Before the use of machine learning, the large database for clinical research may limit to a hundred patients only due to storage of data was the problem. Machine learning solved this problem.
Machine learning can use to diagnose the images. It gives very accurate results.
One of the most important applications in healthcare is the diagnosis of diseases and conditions that are otherwise difficult to diagnose.
This can include anything from malignancies that are difficult to detect in their early stages to other hereditary illnesses. IBM Watson Genomics is a prime illustration of how combining cognitive computing with genome-based tumour sequencing might aid in cancer diagnosis. Berg, a biopharmaceutical behemoth, uses AI to produce therapeutic solutions in areas such as oncology.
- Improving prognostics
Machine learning can use to forecast a patient’s prognosis in addition to diagnosing diseases. Cancer is frequently the first use that springs to mind.
- Drug discovery and manufacturing
Machine learning is implemented at the primary stage of the drug discovery process. It also includes the R&D in gene sequencing and alternative path finding for multifactorial diseases.
Currently, machine learning techniques use unsupervised learning to find patterns in data without making predictions. Microsoft’s Project Hanover is utilizing ML-based technologies for various activities, including the development of AI-based technology for cancer therapy and the personalization of drug combinations for AML (Acute Myeloid Leukemia).
- Outbreak prediction
Machine learning and AI-based technology can use in predicting the outbreak of epidemics around the world. Presently, scientists easily access huge data collected by satellite, real-time social media etc. The artificial neural network enables us to relate the information and prediction of the outbreak of everything around the world.
- Clinical Trial and research
Machine learning offers a number of possible applications in clinical trials and research. Clinical studies, as anyone in the pharmaceutical sector will tell you, are expensive in terms of both time and money, and can take years to complete in many situations.
Using machine learning-based predictive analytics to identify possible clinical trial participants can assist researchers in creating a pool from a wide range of data sources, such as previous doctor visits, social media, and so on. Machine learning has also been used to ensure real-time monitoring and data access for trial participants, as well as to determine the appropriate sample size to be tested and to leverage the potential of electronic records to eliminate data-based errors.
- Better Radiotherapy
One of the most important uses of machine learning is in the field of radiology.
- Use in behavioral modification
Behavioral modification is a key element of preventative medicine, and with the expansion of machine learning in healthcare, a slew of startups have sprung up in the sectors of cancer prevention and detection, patient treatment, and so on.
The present article gives you an overall idea of the use of machine learning for medicinal purposes. Also, the future perspectives of Machine learning in the medicinal revolution are highlighted in this article.