AI in modern medicine eases the workflow in the medical industry. The latest models, such as precision medicine, integrative medicine, and 3D printing in medicine, help medical professionals find unique disease risks. The developments in the Biopharmaceutical industry have revolutionized the treatment of diseases in the present era. Let’s look more into the use cases and applications of Generative AI in modern medicine.
CEO Insights magazine recently interacted with Dr (Professor) Ramneek Ahluwalia, Chief Executive Officer, Higher Health. He says, “The medical industry is a huge industry that will take full advantage of the fruits of artificial intelligence. But there is something called the human element that you cannot remove in this industry. However, the preventive medicine industry will be completely dependent on AI very soon, as will the education industry. Businesses that are going to invest in education or the medical industry will see the fruits very quickly. They will invest in artificial intelligence at the cost of human elements, ensuring the growth of the industry.”
Dr.Ahluwalia continues, “The older generation, when we were growing up in this industry, was completely human-centered. You have nurses, you have doctors, etc. Today, the industry has portable CT scanners that can capture 360 degrees of the human body, or a retinal scan will be a full data indicator and send that information to a doctor or whoever wants to. The human element or the doctor should study the severity of the disease through the touch of the skin. So artificial intelligence will not completely take over the burden of humans in the medical industry.”
Generative AI is revolutionizing healthcare by enabling faster diagnosis and personalized treatment. The use of Big Data and AI has greatly facilitated both the processing and analysis of the vast amount of information generated by patients. The implementation of these techniques in medical care will allow the transformation of the way of diagnosis, in addition to the personalization of treatment, help to identify risk factors and generally improve the results and productivity of the health sector.
Health centers are generally overcrowded with people waiting for treatment for a long time. This causes most patients to rate their healthcare experience negatively. Helping to tackle this predicament, AI has become an integral part of modern medicine with the latest advancements.
ChatGPT Model in the Modern Medicine
Over the years, artificial intelligence (AI) has driven revolutionary advances in various industries, and its impact on healthcare may be particularly profound. Among the rapidly evolving AI technologies, generative AI models such as the Generative Pre-trained Transformer (GPT) models developed by OpenAI with the popular ChatGPT model receiving the most attention have emerged as powerful tools with the potential to reshape the healthcare landscape due to their remarkable natural language processing (NLP) capabilities.
These advanced language models show an unusual ability to understand and generate human text, making them ideal candidates for many applications, especially in medicine and healthcare. By leveraging vast amounts of medical data and knowledge, GPT models can transform various aspects of the healthcare industry and offer a new era of clinical decision support, patient communication, and data management. Their potential to process and interpret complex medical information has fueled optimism about their transformative impact on healthcare practice.
Through the application of clinical digital support, GPT models can help healthcare professionals formulate their suggestions to optimize their decisions, helping them to refine decisions. However, the human element is still needed for disease prognosis and diagnosis.
Other Use Cases
Facilitating Medical Training and Simulation
Generative AI in healthcare can come with realistic simulations replicating a wide range of medical conditions, allowing medical students and professionals to practice in a controlled, risk-free environment. Artificial intelligence can generate models of patients with various diseases or help simulate surgery or other medical procedures.
Traditional training involves pre-programmed scenarios that are limited. AI, on the other hand, can quickly generate patient cases and adapt in real-time in response to decisions students make. This creates a more challenging and authentic learning experience.
Assisting in Clinical Diagnosis
Creating high-quality medical images: Hospitals can use generative AI tools to improve diagnostic capabilities. The technology can convert low-quality scans into high-resolution medical images with great detail, use artificial intelligence algorithms to detect anomalies, and present the results to radiologists.
Disease diagnosis: Researchers can train generative AI models on medical images, lab tests, and other patient data to detect and diagnose the early onset of various health conditions. These algorithms can detect skin cancer, lung cancer, hidden fractures, early signs of Alzheimer's disease, diabetic retinopathy, and more. Additionally, AI models can uncover biomarkers that may cause specific disorders and predict disease progression.
Contributing to Drug Development
According to the Congressional Budget Office, the process of developing new drugs costs an average of $1 billion to $2 billion, including failed drugs. Fortunately, there is evidence that AI has the potential to nearly halve the time it takes to design and test new drugs, saving the pharmaceutical industry around $26 billion a year in annual expenses in the process. In addition, this technology can reduce costs associated with clinical trials by $28 billion per year.
Automating Administrative Tasks
This is one of the most significant cases of generative use of AI in healthcare. Studies show that the burnout rate among doctors in the US has reached a whopping 62 percent. Doctors suffering from this condition are more likely to be involved in incidents endangering their patients and are more prone to excessive alcohol consumption and suicidal thoughts.
Fortunately, generative artificial intelligence in healthcare can partially alleviate the burden on doctors by streamlining administrative tasks. It can also reduce administrative costs, which Health Affairs estimates account for 15-30 percent of total healthcare spending.
Generating Synthetic Medical Data
Generative AI in healthcare can come with realistic simulations replicating a wide range of medical conditions, allowing medical students and professionals to practice in a controlled, risk-free environment. Artificial intelligence can generate models of patients with various diseases or help simulate surgery or other medical procedures.
Traditional training involves pre-programmed scenarios that are limited. AI, on the other hand, can quickly generate patient cases and adapt in real-time in response to decisions students make. This creates a more challenging and authentic learning experience.