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How the Large Language Models like GPT are revolutionising the AI space in all domains (BFSI, Pharma, and HealthCare)

Large language models or LLMs are ushering in a widespread AI revolution throughout multiple business and industry domains. DALL-E-2 set the cat amongst the pigeons in the AI segment in July 2022, developed by OpenAI, before ChatGPT came into the picture. This has put the spotlight firmly on the invaluable role increasingly played by LLMs (large language models) across diverse sectors. Here’s examining the phenomenon in greater detail. 

LLMs make a sizeable impact worldwide

With natural language processing, machine learning, deep learning, and predictive analytics among other advanced tools, LLM neural networks are steadily widening the scope of impact of AI across the BFSI (banking, financial services, and insurance), pharma, healthcare, robotics, and gaming sectors among others. 

Large language models are learning-based algorithms which can identify, summarise, predict, translate, and generate languages with the help of massive text-based datasets with negligible supervision and training. They are also taking care of varied tasks including answering queries, identifying and generating images, sounds, and text with accuracy, and also taking care of things like text-to-text, text-to-video, text-to-3D, and digital biology.

LLMs are highly flexible while being able to successfully provide deep domain queries along with translating languages, understanding and summarising documents, writing text, and also computing various programs as per experts. 

ChatGPT heralded a major shift in LLM usage since it works as a foundation of transformer neural networks and generative AI. It is now disrupting several enterprise applications simultaneously. These models are now combining scalable and easy architectures with AI hardware, customisable systems, frameworks, and automation with AI-based specialised infrastructure, making it possible to deploy and scale up the usage of LLMs throughout several mainstream enterprise and commercial applications via private and public clouds, and also through APIs. 

How LLMs are disrupting sectors like healthcare, pharma, BFSI, and more

Large language models are increasingly being hailed as massive disruptors throughout multiple sectors. Here are some aspects worth noting in this regard: 

Pharma and Life Sciences: 

• Vast neural networks are now being trained in chemical and bio-molecular data, with abilities to understand the same and identify new insights and patterns related to human health and biological sequences. This is naturally accelerating and scaling up drug discovery and research activity alike. 
• They can help identify drug candidates more accurately and swiftly than conventional methods. 
• These tools may also automate several functions including screening of prospective drug candidates while also enabling better identification of relationships and patterns. 
• Through better drug discovery and more efficiency, AI-based LLMs can eventually enhance patient outcomes. 
• LLMs like ChatGPT can enable better supply chain management for the pharma sector, by identifying potential problems beforehand, while enabling the streamlining of supply chains by discovering areas for scaling up efficiencies. 
• There is more personalized patient engagement and therapy, with these tools enabling medical professionals to customize individual patient treatments by analyzing patient information and genetic data. With highly advanced algorithms, it may swiftly analyze vast data volumes for identifying patterns and prospective options for treatments that may not have been clear otherwise. 
• LLMs can also play a vital role in terms of HCP engagement, especially with regard to the delivery of timely, relevant, and crucial medical data to healthcare providers. This also includes data about new medicines, their applications, side effects, and so on. AI tools can help automate this process. 

Healthcare: 

The impact of ChatGPT and other tools in healthcare becomes even more important when you consider how close to 1/3rd of adults in the U.S. alone, looking for medical advice online for self-diagnosis, with just 50% of them subsequently taking advice from physicians. 

• LLMs can help create virtual telemedicine assistants for treatments, appointments, managing health data, and more, thereby helping enhance patient experiences, with better remote health monitoring and treatments. 
• LMs may also enable evidence-based real-time recommendations for providers to boost outcomes with patients. From treatment choices for specific conditions to identifying drug interactions and abiding by clinical regulations, there are many support functions that may be performed in this regard. 
• Healthcare stakeholders can generate automated patient interaction and medical history data and summaries. Recordkeeping and maintenance are greatly simplified with these models. LLMs may also play a vital role in medical translation including technical terms, jargon, and other expressions, helping patients understand things better. 
• ChatGPT and other tools may help patients manage medication and follow doctors’ instructions, especially if they are on multiple medicines on a regular basis. These tools may also help track health on a real-time basis while identifying potential disease outbreaks worldwide and enabling quick responses. Patterns may be identified that indicate new diseases or the spread of any existing illness. There could be a system of automated alerts for all major stakeholders in this case as well. 
• LLMs may help doctors and professionals write reports, document them, and offer real-time insights and so on. They may also help with clinical trial recruitment, tracking symptoms and developing virtual checkers for symptoms, and drug-related information and awareness. 
• ChatGPT and other LLMs may help automate various tasks, enabling doctors to spend more time on actual interactions with patients. They have the ability to enable preventive care, flag potential patient issues and monitor in real-time, along with helping identify symptoms, and boosting post-recovery care. 
• AI-based virtual assistants and Chatbots may help motivate patients and interact with them for better experiences, reviewing their symptoms and recommending diagnostic solutions including direct visits or virtual interactions. 
• These assistants may also respond to all queries of patients regarding medical aspects while sharing news and giving them attention round-the-clock on behalf of healthcare providers. They may also enhance patient engagement with timely information, health maintenance advice, automated reminders, and more. 

BFS: 

• These models can analyse customer information to offer personalised recommendations for higher loyalty and engagement alike. 
• These tools can also support banks in the prevention and detection of fraud through the analysis of massive transaction data and flagging suspicious behaviour or patterns. This will help banks and financial institutions safeguard the assets of consumers while lowering fraud-related losses. Banks can set up systems for alerts in order to enable professionals to get timely notifications of suspicious activity patterns. 
• Loan origination is a complicated procedure, which covers various steps, including credit score analysis, collection of consumer data, risk assessment, loan application processing, and more. Through tapping ML and NLP, banks can automate these tasks, enabling smoother loan origination procedures. Upon applying for loans, customers can get support in real time throughout the entire procedure. Banks can offer feedback on loan applications in real-time along with collecting consumer data and analyzing creditworthiness swiftly. With accurate predictive abilities, these tools can help banks and institutions lower default risks and take more informed decisions regarding loan approvals. 
• LLMs may support banks in enabling customized wealth management solutions for customers through the analysis of relevant data and ensuring personalized investment recommendations based on individual needs and risk tolerance levels. 
• Banks can also take care of KYC and AML (anti-money laundering) procedures through automation and analysis of vast consumer data, identifying future compliance problems, and collecting personal data and transaction histories. It may also enable the verification of consumer identity, checking customers against bank sanction lists, identifying suspicious transactions, lowering error-related risks and enhancing compliance check accuracy. 
• LLMs can help immensely with onboarding, enabling customers to fill out forms online, get answers to their queries, help open new accounts, complete application steps, handle verifications, and offer personalised onboarding experiences. 
• LLMs can also enable 24-7 virtual assistants for customer support. 

Insurance: 

• Large language models (LLMs) may enable virtual assistants and Chatbots for better customer service and user experiences. 
• They can help with data analysis for fraud detection in real-time, plugging losses, and detecting any anomalies swiftly. 
• They can help with risk management, enabling better insights into prospective risks. 
• LLMs may be used for automating things like insurance claims, underwriting, loan applications, and the like, for saving money, time, and energy, while ensuring higher accuracy simultaneously. 
• Right from onboarding and KYC checks to verification and risk assessment, the entire procedure can be automated with these tools. 

The future should witness higher LLM adoption throughout varied business sectors. AI will be a never-ending blank canvas on which businesses will function more efficiently and smartly towards future growth and customer satisfaction alike. The practical value and potential of LLMs go far beyond image and text generation. They can be major new-gen disruptors in almost every space. 

FAQs

What are large language models?

Large language models or LLMs are specialised language frameworks that have neural networks with multiple parameters that are trained on vast amounts of unlabelled text with the usage of self-supervised learning. 

How are they limited and what are the challenges they encounter?

LLMs have to be contextual and relevant to various industries, which necessitates better training. Personal data security risks, inconsistencies in accuracy, limited levels of controllability, and lack of proper training data are limitations and challenges that need to be overcome. 

How cost-effective are the Large Language Models?

While building an LLM does require sizeable costs, the end-savings for the organisation are considerable, right from saving costs on human resources and functions to automating diverse tasks. 

What are some potential ethical concerns surrounding the use of large language models in various industries?

Some concerns include data privacy, security, consent management, and so on. At the same time, there are concerns regarding these models replicating several stereotypes and biases since they are trained using vast datasets. This may lead to discriminatory or inaccurate results at times in their language.