Generative AI in Pharma
Introduction
Generative AI is a type of artificial intelligence (AI) that can create new content, such as text, images, or music. Generative AI models learn from existing data to create new data that is similar in style and content. For example, a generative AI model could be trained on a dataset of images of dogs to create new images of dogs.
Generative AI is a rapidly developing technology that has the potential to revolutionize many industries, including the pharmaceutical industry. Generative AI can be used to create new drugs, improve the efficiency of drug discovery, and personalize treatments for patients.
One of the most promising applications of generative AI in the pharmaceutical industry is the development of new drugs. Generative AI can be used to create new chemical compounds that have the potential to be more effective and less harmful than existing drugs. Generative AI can also be used to design new drug delivery systems that can improve the efficacy and safety of drugs.
Generative AI can also be used to improve the efficiency of drug discovery. Generative AI can be used to screen large databases of chemical compounds to identify potential drug candidates. Generative AI can also be used to design experiments and analyze data. This can help pharmaceutical companies to discover new drugs more quickly and efficiently.
Finally, generative AI can be used to personalize treatments for patients. Generative AI can be used to create models of the human genome and predict how patients will respond to different drugs. This information can be used to develop personalized treatment plans that are more effective and less harmful than traditional treatments.
Generative AI is a powerful technology that has the potential to revolutionize the pharmaceutical industry. Generative AI can be used to create new drugs, improve the efficiency of drug discovery, and personalize treatments for patients. As generative AI technology continues to develop, we can expect to see even more innovative and effective applications of this technology in the years to come.
Here are some specific examples of how generative AI is being used in the pharmaceutical industry today:
• In 2020, a team of researchers from the University of California, San Francisco used generative AI to create a new drug that is effective against a type of cancer that is resistant to traditional treatments.
• In 2021, a team of researchers from the Massachusetts Institute of Technology used generative AI to design a new drug delivery system that can improve the efficacy and safety of cancer drugs.
• In 2022, a team of researchers from the University of Oxford used generative AI to create a personalized treatment plan for a patient with cystic fibrosis.
These are just a few examples of how generative AI is being used in the pharmaceutical industry today. As generative AI technology continues to develop, we can expect to see even more innovative and effective applications of this technology in the years to come.
Generative AI’s Breakthrough Potential in Pharma Marketing
Embrace the wave & pay attention to market.
It is imperative that one refrains from dismissing this as a transient fad, regardless of the circumstances. As per a report published by Reuters in late January, ChatGPT has garnered an approximate count of 100 million active monthly users within a mere two months of its launch on November 30.2 According to a research paper from a bank owned by UBS, this renders it the consumer application with the most rapid growth in history.2 As a benchmark, TikTok achieved a monthly active user base of 100 million within a span of nine months.2 During a year characterized by market downturns in the realm of cryptocurrencies and extensive workforce reductions in the information technology sector, Open AI demonstrated a remarkable level of marketing expertise by facilitating the testing, training, and utilization of ChatGPT by its users. The ultimate outcome was a remarkable user community, copious amounts of publicity obtained through non-paid channels, and an instantaneous correlation between a novel brand and advanced, enjoyable, and beneficial state-of-the-art technology. On January 23, Microsoft announced a multi-year and multi-billion-dollar investment in OpenAI. The aforementioned statement denotes the third instance of the company's commitment, subsequent to prior investments made in the years 2021 and 2019. As a result of these financial undertakings, OpenAI has established an exclusive association with the cloud-based platform Azure, which is proffered by Microsoft.
The floodgates have opened. The AI war has begun.
Google had a obligation to provide a response. On February 6th, the company Bard's response to Open AI's ChatGPT was disclosed. The initial trial of Bard was unsuccessful, potentially due to its hasty execution, leading to a significant divestment of Alphabet shares by investors, amounting to a total of $100 billion.
Indeed, the domains of marketing and sales. There seem to be numerous potential applications for it. In a recent publication from 2019, Faruk Capan, the CEO of EVERSANA INTOUCH and the chief innovation officer of EVERSANA, discussed the notion of R.E.A.L. (recurring, exempt from risk, agonizingly slow, or loathsome) during a podcast interview. Artificial intelligence has the capability to manage such tasks, thereby enabling team members to allocate their time more efficiently towards their areas of expertise.
Please consult the accompanying infographic for a comprehensive enumeration of supplementary advantages that surpass the primary benefit and demonstrate the potential of Artificial Intelligence (AI) in the implementation of sales and marketing strategies in the industry.
Implementing generative AI is essential for pharma
The emergence of generative artificial intelligence (AI) is causing a significant shift in content automation practices across various industries. In particular, the pharmaceutical sector stands to be profoundly impacted by this technology, potentially leading to a transformation in their traditional operational strategies. The implementation of automation within a heavily regulated document ecosystem is a multifaceted undertaking that warrants careful consideration. Although the pharmaceutical industry stands to benefit greatly from this approach, it is important to exercise prudence. Incorporating LLM components into pharmaceutical workflows requires a provider that adopts a dual approach, considering both data and human factors. This is crucial for the success of pharmaceutical companies. The utilization of LLMs and symbolic AI in model training is imperative to guarantee the dependability and legitimacy of the outcomes. It is imperative to consider a diverse array of fundamental customer prerequisites, including but not limited to data segregation and protection, lucidity and comprehensibility of AI models, impartiality and equity, and the ability to scrutinise discoveries. Due to the confidential nature of patient information, it is imperative for medical writers to utilise a secure and reliable solution.
As per the statement of Brian Burke, who holds the position of Research Vice President for Technology Innovation at Gartner, it is anticipated that generative AI solutions will be accountable for the identification of more than thirty percent of new pharmaceuticals and materials by the year 2025. This percentage is expected to rise from the current zero. The potential impact of generative AI on the pharmaceutical industry and other heavily regulated businesses is significant, although the future of this technology remains uncertain.
Recent Innovations using Generative AI in Pharma
The renewed interest and ethical considerations regarding Generative AI may potentially overshadow the numerous beneficial outcomes that AI technological tools can achieve, especially in sectors like healthcare and pharmaceuticals.
Nvidia has introduced the BioNeMo Cloud Service, aimed at facilitating drug discovery by offering pre-trained AI models to drug researchers. This service aims to streamline the drug discovery cycle and enhance its efficacy.
According to a publication on the Nvidia blog, generative artificial intelligence (AI) models possess the capability to swiftly detect potential drug molecules, and additionally have the ability to create compounds or protein-based therapeutics from the ground up. The aforementioned models possess the capability to forecast the protein's three-dimensional configuration and the efficacy of a molecule's docking with a target protein. This is due to their training on extensive datasets comprising small molecules, proteins, DNA, and RNA sequences.
Amgen and other companies are currently utilizing the BioNeMo service.
BioNeMo offers a unified interface that provides optimized models and model hosting, enabling drug discovery teams to efficiently deploy and expand Generative AI workloads. Additionally, enterprises can train and refine custom models using their proprietary data, leveraging their extensive research and data resources. The platform's advanced features and streamlined interface make it a valuable tool for drug discovery and related fields. Nvidia has formed partnerships with enterprises that express a keen interest in augmenting medical devices and procedures through the utilization of artificial intelligence technologies. At the NVIDIA GTC, a prominent international conference on artificial intelligence, NVIDIA disclosed its collaboration with Medtronic, a leading medical device technology enterprise and an enthusiastic adopter of AI, to incorporate edge AI functionalities into Medtronic's GI Genius intelligent endoscopy module, which is created and produced by Cosmo Pharmaceuticals.
The FDA has granted clearance for the use of GI Genius, an AI-assisted colonoscopy tool, to aid healthcare professionals in identifying polyps that could potentially lead to colorectal cancer. This marks the first instance of such a tool being approved for clinical use. According to Nvidia, the integration of the Nvidia Clara healthcare platform has the potential to facilitate the scaling up of AI algorithm development for real-time procedures by Medtronic, which could lead to an acceleration of AI innovation for enhanced patient care. The GI Genius platform was developed with the purpose of accommodating a collection of artificial intelligence algorithms.
Furthermore, the integration of Nvidia Holoscan, a software platform that utilizes real-time AI computing for the development of medical devices, and Nvidia IGX, an industrial-grade edge AI hardware platform, will be utilized in conjunction with GI Genius to provide medical practitioners with AI-enhanced diagnostic images. Both of these platforms have been specifically developed to be utilized in tandem with GI Genius. As per Nvidia, Holoscan provides users with complete access to the infrastructure stack essential for the scalable and software-defined processing of streaming data at the edge. It is expected that the integrated solution will become available later this year. This development is expected to contribute to the emergence of a new era marked by a higher incidence of software-defined medical equipment and procedures.
Challenges in Implementing
The showcased advancements in LLM technology by ChatGPT have the potential to significantly augment the quantity of applications. However, there exist several noteworthy impediments that must be addressed before LLMs can be established as a customary practices in the pharmaceutical industry.
Accessing data sets in the pharmaceutical industry poses a challenge. Pharmaceutical marketers encounter challenges with proprietary data sources that often lack interoperability, while a significant proportion of generative artificial intelligence models have not undergone training on datasets specific to the life sciences industry.
Ethical considerations. Marketers exhibit a sense of prudence in embracing generative AI technology, owing to their apprehensions regarding the potential for the content generated to be misleading.
The regulations pertaining to the protection of data privacy. It is incumbent upon pharmaceutical marketers to ensure that their utilization of Generative Artificial Intelligence aligns with relevant data privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in the European Union.
Conclusion
As previously stated, the utilization of GPT technology has facilitated extensive speculation and experimentation, resulting in notable impacts on the pharmaceutical industry. Undoubtedly, GPT-4 is poised to expand the realm of possibilities beyond what was previously feasible. According to reports, it possesses exceptional abilities in both the domains of visual identification and comprehension. The system has the capacity to accommodate a maximum of 64,000 words to be utilized as contextual information. In comparison to GPT-3, there is an eightfold augmentation. The capacity to provide responses to inquiries in 26 distinct languages. The model is deemed to possess greater reliability, novelty, and efficacy in its academic metrics, which are deemed to be analogous to those of human beings.
Similar to how an artist's palette presents boundless prospects for artistic expression, GPT-4 uncovers a vast expanse of potentialities that remain uncharted. What novel artistic creations will emerge as a consequence of the development of GPT4?
References
r. Arivukkarasan
A versatile business professional with over 20 years of experience in Data Analytics, Robotics, IoT, Machine Learning & Human Resource Management.
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