How is AI strengthening the realm of biotechnology?
Artificial Intelligence and machine learning have been shaping biotechnology in recent years significantly. In the last decade, several new applications have been developed using AI, which can leverage biotechnology.
A survey carried out on life science and pharma experts reveal that 44% of these professionals are presently using AI in research and development activities. From drug development to personalised prescription, AI has been streamlining healthcare by boosting biotechnology.
Data used in the healthcare industry comes from several sources. This includes research data, caregivers, patients, doctors and hospitals. It is quite challenging to compile all this information and format them for further utility. In order to come up with better protocols and healthcare networks, it is necessary to embrace AI technologies.
Let’s explore how biotechnology has been beneficial to biotechnology
Diagnosis processes have turned out to be less invasive and faster, thanks to AI. It helps in diagnosing diseases faster. For instance, medical practitioners can now detect cancer in the molecular stages. This helps the doctors treat it, before it advances to the tumour stage.
Based on DNA, several diagnostic tests are being carried out. This technology has helped in yielding pure and well-defined antigens that provide more precise interpretations in diagnostic tests.
- Discovery of drugs
One of the best breakthroughs of AI in biotechnology has been the discovery of drugs. The process involves several mechanisms, like screening of peptides and other materials.Research carried out on these aspects show that drug discover processes have been enhanced by AI.
Besides, doctors can now prescribe personalised medicines to the patients. These are tailored specifically to a person, factoring the genetic makeup, environmental factors and biological factors. The healthcare organisations are now using a structure-oriented approach for discovering new drugs.
Machine learning can help in the discovery of smaller molecules of high therapeutic value. Other applications of AI in biotechnology include clinical trial design and optimisation of the same.
- Electronic health record
In leading healthcare organisations, support systems based on clinical information and evidence-oriented medicines are being used. The electronic health record mechanism is powerful, enabling the medical professionals make informed decisions, considering the clinical history and preferences of a patient.
Digital automation and AI can also effectively manage medical records. Data pours into the healthcare sectors from several sources. EHR can effectively store and manage this data through digital automation. This enhances the quality of care that the patients receive.
Previously, the absence of synchronicity between companies and medical professionals was one of the biggest obstacles that healthcare professionals faced. AI developers working in the healthcare segment should understand the nature of data being used in the industry. Evidently, automated EHR systems for data management have made their way into biotechnology and healthcare.
- Clinical trials
The process of clinical trial research is arduous and long. Machine learning has substantially streamlined the process. Through advanced predictive analytics, clinical trials can be carried out on target populations in quick time.
Besides, analysts believe that other AI-based technologies are coming up, that will help in calculating the right sizes of samples. It will help in treating the patients and minimise errors in data by using medical records.
- Customised treatments
As stated, personalised medicines based on AI can now be prescribed. A lot of research is being carried out on the use of machine learning to customise the treatment of patients, based on unique health records. If the attempt is successful, treatment protocols and diagnoses will further be optimised.
Presently, the focus lies on supervised learning, where genetic symptoms and information are used by the doctors. This enables them to narrow down the options of diagnosis, or to make an informed guess regarding the risks that a patient might be facing. In the process, they can take preventive measures.
You must have heard of IBM Watson Oncology, where the medical record of a patient is used along with personal inputs for recommending the best treatment.
- Gene editing
Genetics largely determine the illnesses that a person is likely to suffer from during the lifetime. In recent years, gene editing has been an interesting aspect of research. The medical practitioners and experts working on biotechnology need to get a better interpretation of the genetic makeup of patients.
AI fosters a seamless understanding of this aspect. The enormity and complexity of data that needs to be evaluated used to be a hurdle in gene editing in the past. AI and machine learning have largely helped in carrying out advancements in this aspect.
Development in AI techniques has enabled the researchers understand and take actions on genomic data through gene editing and genome sequencing. This has been one of the biggest breakthroughs of biotechnology in recent decades.
- Radiology and radiotherapy
Integrating AI techniques, the process of radiation therapy planning has been reduced to a few minutes. This helps radiologists in saving time. In the past, it took them several days to enhance patient care.
Particularly, it has enhanced the treatment process of cancer significantly. In the next couple of decades, radiologists might experience a complete change in the tasks they carry out. They might resemble cyborgs, reading tons of studies using algorithms each minute.
Presently, machine learning algorithms are being developed to enhance the accuracy of radiotherapy planning, drawing a line between cancerous and healthy tissues.
Challenges that biotechnology might still face
Technological hurdles still continue to be an issue, as long as advancements in healthcare and life sciences are concerned.
Developments in AI and machine learning are to be translated into applications across the world. In reputed hospitals, some of these techniques are already in use. However, it often turns out difficult to access personal medical information.
A research, however, reveals that in 83% of the people are interested to share their medical data for research purposes, so long they remain anonymous. Transparent algorithms are necessary for regulations related to the development of drugs.
Therefore, people should understand the mechanism in machine learning.In the coming years, further development in ML algorithms will enhance the precision of radiotherapy planning, bolstering biotechnology applications.
Mukesh Kumar Sinha, Co- Founder & COO, Gravitas AI