Artificial Intelligence (AI) has taken the world by storm by making machines learn from experiences, adapt to new responses, and execute human-like tasks. Most of the AI we experience today across industries relies heavily on deep learning, natural-language processing, statistical analysis, and algorithmic executions.
Generative AI models are a step further into AI advancement and use neural networks to identify the patterns and structures within existing data to generate new and original content. Post-pandemic the healthcare industry has embraced generative AI much faster than anticipated. The possibilities for revolutionizing drug discovery and personalized medicine are becoming increasingly evident with generative AI. By harnessing the power of AI algorithms, the industry can analyze a vast amount of data and uncover novel insights that were previously unattainable.
There are 2 main areas in the healthcare landscape where generative AI is transforming dynamics.
- Drug Discovery
- Personalized Medicine
1. Drug discovery:
Traditionally, the process of developing new drugs has been a painstakingly slow and costly endeavor. However, with generative AI, pharmaceutical (biotechnological) companies are leveraging AI algorithms to expedite the drug discovery process and identify the most potential drug molecule.
Insilico Medicine, a biotechnology company, used generative AI to develop a novel chemical compound with the potential to treat fibrosis. The company analyzed vast molecular databases and the AI system further generated unique structures (chemical compounds) that were further optimized through virtual screening and predictive modeling. The breakthrough of one of its kind was published earlier this year through a paper named “Chemistry42: An AI-Driven Platform for Molecular Design and Optimization”.
DeepMind’s AlphaFold, a subsidiary of Alphabet Inc. developed an AI system that predicts protein structures. Protein plays a crucial role in human existence and their structures determine their functions and interactions with any kind of administered drugs. The generative AI uses a deep-learning algorithm to predict protein folding, enabling researchers to understand protein structure more accurately and efficiently. This technology holds great potential in the drug discovery process, which can accelerate the process by facilitating the identification of suitable drug targets and aid in the design of effective therapeutics within a faster time frame.
Atomwise is another example of a generative AI platform that analyzes the molecular structure and generates novel compounds that have the potential to act as drugs. Recently, the company has collaborated with the researchers at University of California to identify potential treatments for the Ebola virus.
The above breakthrough is clearly indicative of the fact that generative AI can accelerate the drug discovery process and has a major role to play in bringing out life-saving medications to patients faster than ever before.
2. Personalized medicine
Personalized medicine varies from conventional medicine, as these are the kind of medicines that use a person’s own genes or proteins to prevent, diagnose or treat disease. Generative AI is also transforming personalized medicine by tailoring treatments to individual patients.
One of the most significant AI platforms is IBM Watson, which is deployed and used in precision oncology (cancer treatment). The platform analyzes vast amounts of patient data, including medical records, genetic information, and research papers by applying generative AI algorithms. Watson generates personalized treatment plans considering patient-specific genetic profiles, disease characteristics, and potential drug interactions. The approach thus ensures that each patient receives tailored therapy, maximizing treatment effectiveness and improving patient outcomes.
Generative AI is recently making headlines within pharmacogenomics where AI algorithms are used to analyze an individual’s genetic information. The algorithm can identify specific genetic variations with an individual’s genetic profile and thus can suggest recommendations considering drug metabolism, efficacy, and potential side effects of a particular drug, making medication safer for patients.
However, it is crucial to remain mindful of the ethical implications and potential challenges that accompany the use of generative AI in healthcare. Upholding data privacy, addressing biases within algorithms, and maintaining a balance between AI-driven insights and human expertise are pivotal considerations. Striking this delicate balance will foster trust in technology, enabling its seamless integration into the healthcare ecosystem while upholding the highest standards of patient care and safety.
The impact of generative AI extends beyond individual patient care. In the realm of medical research, the ability to synthesize vast amounts of data from various sources is invaluable. The AI systems can analyze and interpret a wide range of information, including research papers, clinical trials, electronic health records, and medical imaging data, by uncovering patterns, correlations, and hidden insights, generative AI empowers researchers and healthcare professionals to gain a deeper understanding of the disease, identify new avenues for exploration and help in making more informed decisions in terms of diagnosis and treatment.
At Netscribes, our team of AI specialists work in tandem with healthcare trends to develop customized solutions leveraging the power of generative AI to drive meaningful results. Right from automating repetitive tasks, generating insights faster, or improving the end-user experience, our AI Business solutions can be tailored as per your business needs.
Ready to revolutionize your healthcare operations? Contact us today and discover how we can help you build a healthier future.