Why do we – as a human civilization – need to nurture scientific competencies and foster R&D-driven innovation? Can’t conventional techniques and approaches be followed for eternity?
Well, the very purpose of science and technology is to uplift humans, elevate lifestyles, and ultimately make the world a better place. Specifically, in the realm of healthcare, scientific advancements are what helps us evolve into smarter and healthier species in the visions of Darwin.
And right now, we are at the cusp of such a transformative era. This is the age of Artificial Intelligence (AI) and its myriad applications and use cases such as Large Language Models in healthcare. With the use of such technology, we are closer to solving age-old mysteries relating to the human body, discovering drugs to treat terminal illnesses, and even defying aging.
So, buckle up for an interesting article today as we explore the role of LLMs in clinical applications, and how it enables scientific evolution.
Interesting Statistics On AI In Healthcare
- The use of AI in clinics and healthcare centers has significantly reduced the time spent on redundant administrative tasks by 20%.
- Over 90% of hospitals are anticipated to deploy AI-driven applications to improve remote patient monitoring by the year 2025.
- AI can reduce expenses incurred in the discovery of new drugs by 70%.
Use Cases Of AI and Large Language Models In Healthcare
To understand LLMs in healthcare better, let’s quickly recall what LLMs are. Developed through deep learning techniques, LLMs are designed to manipulate humans and human language. They are named Large because of the incredible volumes of data they have been trained on.
To simplify understanding, imagine GPT-4.o or Gemini for healthcare. When such bespoke models are deployed for super-specific, niche requirements, the avenues of possibilities are aplenty. Let’s look at some of the most prominent use cases.
Clinical Decision Support
The role of AI in healthcare diagnostics is game-changing. One of the fascinating advantages of LLMS is that they can detect or identify patterns and anomalies that go unnoticed to the human eye. With the input of precise data, LLMs in healthcare can aid in supporting clinical decisions by analyzing patient data and suggesting diagnoses.
This is specifically super-precise with respect to radiology, pathology, and other medical imaging reports.
AI-powered Medical Assistants
Over the last few years, awareness and understanding of individual bodies have increased. This is primarily due to the rise of wearable devices that visualize otherwise abstract body-generated data and is further propelled by mhealth or telemedicine.
Through medical applications and healthcare marketplaces, people are increasingly resorting to telemedicine facilities. To engage such patients and deliver precision healthcare, robust systems are needed. LLMs can help healthcare organizations achieve this. Through the use of chatbots or specific medical assistants, healthcare experts can implement and optimize clinical workflow automation.
This can help in:
- Understanding basic details about a patient
- Retaining and recalling the medical history of patients
- Scheduling appointments and sending nudges and reminders
- Retrieving accurate information about patient conditions and assisting them in their recovery and prognosis
- Answering FAQs on their conditions and more
AI For Drug Discovery
Discovering drugs for diseases is more complex than what we can comprehend. It is rigid, and systematic, and involves overwhelming volumes of protocols, processes, and procedures. It is also extremely sensitive and study and research-driven.
However, with the use of LLMs, healthcare experts can enhance the process of drug discovery in the following ways:
- Identify and understand biological targets through deep learning techniques. This will allow accurate analysis of exposure, reactions, and predictions involving the functioning of the new drug in treating intended ailments.
- LLMs and AI models can generate molecular structures from scratch. This means such structures can be manipulated for their bioavailability, potency, and more. Besides, drug simulations can also help researchers understand reactions and adversaries and even discover drugs for other diseases apart from the one being currently worked on.
- LLMs can also fast-track drug discovery processes by helping researchers understand if existing drugs could be used to treat other ailments. One of the most recent real-time examples of this was the deployment of AI to validate the effectiveness of Remdisivir in treating COVID-19.
- Personalized medication could witness breakthroughs with AI as drugs are tailored to effectively work based on an individual’s genetic, lifestyle, and environmental data.
Support For Mental Health
Apart from physical ailments, the world is undergoing an extreme crisis relating to mental health. With alarming statistics, AI can enable required support through AI-powered medical assistants or virtual companions in terms of awareness, education, and assistance in helping patients and those in suspicion. Level further, it can also help treat PTSD in war veterans and soldiers, disaster-recovered individuals, and more.
Deployment Challenges Of LLMs In Healthcare
- Concerns surrounding the safety and privacy of patient data loom with the increased adoption of AI. It only takes one error, act of negligence, or vulnerability to gain access to volumes of sensitive healthcare data.
- Due to the advantages it offers, it can be convenient for stakeholders and clinics to increase their reliance on AI for diagnosis, patient care, and service delivery. This has to be moderated through regulations and solidifying XAI.
- Around 80% of the healthcare data is unstructured. Challenges lie in standardizing unstructured data and transforming them into machine-ready datasets.
- Integration with existing healthcare systems and modules also poses a technical and logistical challenge for stakeholders and healthcare organizations.
Building Healthcare-specific LLMs With Shaip
Of all the challenges, what is probably the toughest is developing and training such large models to precision. Healthcare involves life and death and one misconfiguration or inappropriate response could trigger negative consequences. That’s exactly where AI training with the right datasets comes into the picture.
Because of regulations such as GDPR and HIPAA, the availability of trainable data is still a bottleneck faced in developing Generative AI for patient care. However, Shaip arrives as a reliable and convenient solution to this conflict.
Our healthcare datasets are ethically sourced, de-identified, and validated by humans. For all your data requirements at scale, explore our offerings and find out how we can provide you with abundant healthcare data to train your medical large language models.