The pharmaceutical industry will be worth US$1.2 trillion by 2024, in terms of total prescription drug sales. The market evaluation of machine learning industry will be worth US$8.81 billion by 2022, as quoted in Machine Learning Report, published by Vertical. Now when you integrate machine learning into the pharmaceutical industry, you can estimate the staggering growth and reforms by simply looking at the research figures.
The machine learning algorithms collect and processes data into information, which will be further used for creating intelligent mechanisms, like pharmacy ERP software, that will be deployed in the pharmaceutical sector. You can expect a boost in innovation, research & development, and other operations in the pharmaceutical industry. How machine learning can transform the pharmaceutical industry, particularly the manufacturing sector? The following applications will provide you detailed insights.
Diagnosis & Therapeutic Treatment
A doctor is capable of diagnosing patients based on their medical history and disease symptoms. However, as a human, a doctor has limited knowledge, thereby, restricting the scope of diagnosis and treatment. Machine learning assists doctors by enhancing their knowledge and skills. Google’s DeepMind Health uses machine learning to collect and analyze visual information and render effective diagnosis and treatment for diseases like cancer and macular degeneration in aging eyes.
Machine learning is immensely beneficial in precision medicine. The technology can be deployed to analyze disease processes and design effective treatment methodologies. MIT Clinical Machine Learning Group is working towards devising effective processes for the treatment of diseases like type-2 diabetes. Their focus is on research & development in precision medicine.
Drug Discovery & Synthesis
Drug discovery, formulation, and manufacturing are inevitable aspects of the pharmaceutical industry. Every day, new drug patents are approved and launched in the market for prescription sales. But if you dig deeper, you will realize the whole process, right from early-stage discovery to drug synthesis, requires intense efforts, resulting in hefty expenditure and time consumption.
Machine learning plays a crucial in simplifying the complete process. It accumulates, analyses, and implements the massive amount of chemical data which, otherwise would have remained undiscovered. Further, it enhances the capability of biologists and researchers to work efficiently, resulting in breakthroughs in the pharmaceutical industry.
Personalized & Effective Treatment
If you can analyze a patient’s symptoms, history, and genetic information; you can adopt a personalized diagnostic and therapeutic procedure. Personal medicine ensures effective treatment by integrating individual health data and predictive analytics. Machine learning finds immense applications when it comes to personalized treatment.
The doctor can instantaneously scan records and write a drug prescription. The patient can procure medicines from the pharmacy, which again, uses machine learning based pharmacy softwareto automate pharmacy operations. ICU Intervene is a tool that collects ICU data, learns from them, and renders real-time predictions. The tool is based on deep learning, a subdomain of machine learning.
Clinical Trials & Monitoring
Clinical trial research is influenced by machine learning. The researchers can identify suitable candidates for research purposes by analyzing information extracted from various sources like social media, health records, data providers, and so on. Further, machine learning can identify and target a specific group of people based on their genetic composition.
Machine learning finds applications in monitoring health data and records, demographics, and other vital information. It is also capable of monitoring conditions that can result in an epidemic outbreak. Artificial Intelligence and Medical Epidemiology (AIME) developed the Dengue Outbreak Prediction platform, capable of predicting dengue outbreak 3 months in advance. Similar applications are the result of constant monitoring and predictive analytics.
Data Segmentation & Analysis
When you have access to a diverse range of datasets, you can effectively compare and evaluate information to take the best course of action. While chemists and researchers have access to propriety datasets, these are not enough to boost the manufacturing process. The machine learning tools can assist them in extracting data from multiple resources like the company’s database, medical literature, data providers, and so on. The huge amount of data allows them to streamline manufacturing processes, for example, designing molecules according to parameters like binding affinity and chemical toxicity.
Conclusion
The machine learning and artificial intelligence enable decision-makers and implementers, in the pharmaceutical domain, to streamline operations and achieve distinguishable feats, thereby, making a significant contribution to the mankind. This is the perfect moment to bring in reforms by investing in machine learning, and transform your pharmaceutical business to gain an edge over your competitors.
Hitendra Rathore is a Software Analyst at SoftwareSuggest. He has spent the majority of his career in SaaS industry gaining experiences in areas such as Accounting Software andERP Software analysis. Outside of the office Hitendra enjoys spending time with his family and listening to music.
