Artificial intelligence is transforming our lives and interactions in our day-to-day life. It is aimed to work as a human-like cognitive process. AI has various aspects: autonomous vehicles, learning, creativity, spatial reasoning, language proficiency, etc.
Artificial Intelligence in life science is taking a new shape by delivering mass biological data with machine learning, various genomic data, clinical records integration, diagnosis, clinical procedure customization, recognizing new cell type classes, and discovering new drugs.
The opportunities for AI in life science are tremendous. Significantly, we have to remember that we are making extra data. At the same time, sequence methods are turning more affordable to simplify producing substantial data sets.
The various applications of AI in life sciences comprise the following:
Generating personalized medicine
Currently, we reside in a world wherein we follow the phenomenon of ‘suitable to be used under all circumstances for medical dosage. Whenever a dosage is set or while designing a therapy, a little information about the patient is considered.
AI can access the digital health records of the patient and deliver the best treatment plan. In addition, by further studying the other parameters, medical practitioners can adjust the dose size. In situations when the disease persists, effective therapy is suggested.
Machine learning tools have come into action to assess unstructured records, such as blood tests, images, genome reports, medical history, etc., that helps doctors meet the needs of the patients.
Disease diagnosis
Increased records and incomplete records lead to wrong predictions and diagnosing diseases. The latest AI chatbots have been built that will listen to patients and their health symptoms. Further, they develop the algorithms and suggest suitable therapy for the patient.
AI can scan the images developed through mammography or radiotherapy and correctly identify the disease.
Robotic surgery
Robotic surgery is a new field creating interest among people. The Da Vinci Surgical System is the much-talked-about surgical system primarily used for cardiac valve repair and gynecologic and renal surgery. The robots are thoroughly trained to perform accurate and consistent surgical processes irrespective of the surgery duration. This potential has to be highly applauded, for it can surpass the
a human capability that comes down with time.
Drug manufacture
Drug development is not easy. The process is both expensive and time-consuming. A lot of time should go into the trial process and outcome and attain the final approval stage. A large number of molecules requires studying. The AI-based program can scan and analyze large complex data sets very quickly and accurately relative to humans—such action results in a more detailed list of drugs in a brief period economically.
Clinical trials
AI plays a significant role in designing clinical trials, estimating the sample size, and executing it remotely across participants. The costs decrease and enhance the chances of attaining the most correct and relevant information.
Logistics and supply chain management
Pharmaceutical companies and drug manufacturers can transform logistics and supply chain management with Artificial Intelligence. For example, AI can make the demand forecast so that the production can scale up or down depending on the need.
Extends healthcare access
A lack of healthcare-trained professionals could put lives at risk. Hence, the need for health professionals such as radiologists and ultrasound technicians exists. For instance, the AI-powered Telemedicine tool has grown popular in tackling health concerns. ‘WeDoctor’ is another healthcare start-up designed to perform eleven diagnostic tests and upload them automatically.
Radiology Equipment
The first radiology AI application is Computer-aided detection (CAD). Its functioning was very rigid, and it could only find the defects in the existing training dataset. It does not perform autonomous learning, and thus every new skill has to be hard-coded.
Since then, radiology has evolved, helping doctors identify brain tumors, find hidden fractures, identify breast cancer during the early stages, detect neuron abnormalities, etc.
Conclusion
Recognize the AI in Life Sciences need for your medical organization. Create your organization’s needs and assess how you can attain them. This will help me explore further and gain the desired development. Technology has created a change in the way you view research, drug development, medicines, etc. Hence it is time to experience how medicines and treatments are quickly identified, developed, and produced.
