Intro
Recent advancements in AI technology have highlighted discussions on AI applications in healthcare. Beyond chatbots and image generation, AI systems have been developed for a whole host of specific and complex tasks such as disease identification and diagnosis.
AI is at the strategic forefront of many industries, especially healthcare where it can be used to improve patient care, increase efficiencies, and reduce physician burnout. Not only can AI technologies analyze vast amounts of medical data, but they can also be trained to effectively identify patterns and detect diseases in the early stages when treatment is more effective.
In this post, we discuss how AI is transforming disease identification and diagnosis and the potential of this technology in improving healthcare outcomes.
Early Detection and Diagnosis of Diseases
One of the primary benefits of AI in healthcare is its ability to identify and diagnose diseases at an early stage while reducing the burden on the physician. With hundreds of thousands of medical images produced each day, radiologists can become overwhelmed with the sheer volume of data to review. AI can help to ease this burden by automating the analysis of these images and highlighting areas that require attention. This can help doctors detect diseases such as cancer and Alzheimer's at an early stage, increasing the chances of successful treatment.
As reported in Scientific American, researchers at Google tested the Med-PaLM AI’s performance on different “axes,” including alignment with medical consensus, completeness, and possibility of harm, in a preprint study. Its answers aligned with medical and scientific consensus 92.6 percent of the time. Human clinicians scored 92.9 percent overall. Chatbot answers were more likely to have missing content than human answers, but the answers were slightly less likely to harm users’ physical or mental health. An earlier version of Med-PaLM and GhatGPT have both passed the U.S. Medical Licensing Exam.
The kinds of disorders and illnesses that AI can detect are varied, both in the systems they affect and the danger they pose to patients. A few of them include:
Cancer
As a leading cause of death worldwide, it’s impossible to overstate the impact that cancer has on individuals and communities. However, the latest developments in medical technology are offering hope in the form of AI analysis of medical imaging. Early detection is key in addressing cancer, and AI algorithms assist in detecting cases with greater accuracy than ever before. They can analyze mammograms to detect breast cancer tumors with 94% accuracy, while Google’s AI model has reduced false negatives by 9.4%. With early detection, skin cancers such as melanoma have a 99% survival rate for localized cases. Prostate and lung cancers can also be diagnosed with greater accuracy through AI-assisted MRI and CT scans. The potential for AI-assisted medical imaging to improve cancer detection and, in turn, treatment represents an exciting step forward in fighting this devastating disease.
Cardiovascular Diseases
Cardiovascular diseases are a major global health concern with almost 19 million deaths each year, making them another leading cause of death. Fortunately, preventative health assessments can be a powerful tool in combating this deadly disease. With the use of machine learning, there are several ways cardiac risks can be predicted and detected. For instance, analysis of ECG data can help to detect arrhythmias. Patient's health records and test results can be evaluated to predict the likelihood of heart failure. Atherosclerotic plaques can be identified in arterial images and wearable devices can monitor vital signs to predict the onset of stroke and heart attacks. Machine learning has already had a significant impact with an 87.6% prediction accuracy in identifying stroke risk. With the help of these predictive tools, healthcare professionals can take proactive measures in preventing and treating cardiovascular diseases.
Neurological Disorders
Diseases like Alzheimer's and Parkinson's have become a critical concern for medical science. While incurable, timely detection can help prepare for and organize quality care to improve overall outcomes. For this reason, machine learning is now deployed as a tool to aid in early diagnosis. In the case of Alzheimer's disease, the technology can analyze brain imaging data to detect early signs of the disease. By demonstrating the early decline of brain function, treatments can be planned before symptoms become apparent. In the case of Parkinson's disease, AI analyzes voice data and hand movements to identify early signs of the disease. With the ability to diagnose the disease before symptoms become apparent, these technologies offer promise in the fight against these debilitating neurological conditions.
Personalized Treatment Plans
Another benefit of AI in healthcare is its ability to analyze patient data and develop personalized treatment plans. While no two bodies are identical, they all function in much the same way. Medical science relies on these expectations to figure out what specific system, organ, or tissue is malfunctioning—the diagnostic step. But it also helps doctors predict, within reason, how a body will respond to treatment. That said, bodies are extremely complex, and treatment for one illness or injury may affect another healthy part of the body.
By analyzing images of a patient's organs, AI systems can predict how a disease is likely to progress and recommend the best course of action. This can help medical professionals to provide a personalized and more effective treatment plan, potentially leading to better outcomes for patients that result in fewer side effects.
Improved Drug Development
Traditional drug development is a long and costly process that can take years to complete because it consists of processing massive quantities of information. AI technologies can help to speed up this process by analyzing vast amounts of data and identifying patterns that otherwise would have been missed. This can help to identify new drug targets and speed up the process of developing new treatments for diseases.
According to the Petrie-Flom Center at Harvard Law, AI is being used in every step of the drug discovery process, from target identification to molecular simulation. It can even bypass certain types of simulated testing on drug candidates predicting properties like toxicity and bioactivity. Since just the pre-clinical drug discovery process takes an average of 3-to-6 years and sometimes billions of dollars, AI is helping researchers save incredible amounts of both time and money in the early stages of drug development.
Reduced Healthcare Costs
AI has the potential to reduce healthcare costs in a few different ways. The previously mentioned improved drug development means that drug costs can be lowered. While the materials needed to make a drug might remain the same price, the cost of production could be lowered. Additionally, by identifying diseases at an early stage, AI diagnostics can help to prevent the need for expensive treatments. It’s often easier and cheaper to treat a mild sinus infection than an advanced case of pneumonia. And finally, by developing personalized treatment plans, AI can help to reduce the number of ineffective treatments prescribed, leading to reduced overall healthcare costs.
At the start of 2023, Healthcare Dive reported that AI could save the healthcare industry up to $360 billion annually, in the United States alone. That number is dependent on a lot of factors, not the least of which is a better sense of trust between AI’s capabilities and the healthcare providers and patients that would make use of it. But AI’s ability to simplify workflow could save tremendous amounts of money in operating costs alone before even accounting for its diagnostic capabilities.
Large Language Model Chatbots in Healthcare
The COVID-19 pandemic has highlighted the need for remote healthcare monitoring and consultations. One of the most promising technologies in this field is the use of Large Language Model (LLM) Chatbots. LLM Chatbots are AI-powered chatbots that use natural language processing (NLP) and machine learning algorithms to understand and respond to patients’ queries. In this blog post, we will discuss how LLM Chatbots can transform healthcare.
Enhanced Patient Experience
Patients can communicate with LLM chatbots using natural language. This eliminates the need for pre-defined questionnaires or forms, making the interaction more natural and seamless. Patients instantly receive answers to their queries, reducing wait time and improving overall patient experience. LLM chatbots can handle a virtually unlimited number of patient interactions at the same time. This significantly reduces the workload of healthcare providers, enabling them to focus on critical cases. Furthermore, LLM chatbots can answer common queries and perform tasks like appointment scheduling, reducing the workload on administrative staff. This can lead to significant cost savings for healthcare institutions.
Using Chatbots in Healthcare Responsibly
For all the help that Chatbots can be within the healthcare industry, many experts emphasize using the technology responsibly so as to ensure risks are appropriately mitigated. One potential risk is susceptibility to hallucination. Depending upon how a chatbot has been trained, it may be prone to hallucination under certain conditions, giving confident responses containing inaccurate or sensitive information. These issues could lead to both patient harm and HIPPA violations.
A potential shortfall of current LLM algorithms is that they predict the next word in a series based on its likelihood of occurring in the training data. This potentially grants equal weight to, say, information from the U.S. Centers for Disease Control and Prevention and unqualified information from social media. A spokesperson for OpenAI told Scientific American that the company “pretrains” its model to ensure it answers as the user intends, but she did not elaborate on whether it gives more weight to certain sources. She adds that professionals in various high-risk fields helped GPT-4 to avoid “hallucinations,” responses in which a model guesses at an answer by creating new information that doesn’t exist. Because of this risk, the company includes a disclaimer saying that ChatGPT should not be used to diagnose serious conditions, provide instructions on how to cure a condition or manage life-threatening issues.
Conclusion
AI technologies are transforming the way we approach disease identification and diagnosis by automating the analysis and interpretation of vast amounts of data. With the potential to improve patient outcomes and reduce healthcare costs, AI is poised to revolutionize the way we approach healthcare in the coming years.
As the use of artificial intelligence (AI) continues to expand in healthcare, it is imperative to remember that AI is a tool to supplement the work of qualified medical personnel, not a replacement. Nevertheless, AI has the potential to provide invaluable assistance in diagnosing complex medical conditions, ultimately improving patient care and outcomes, and reducing physician burnout. The future of AI in healthcare requires consideration of ethical concerns such as patient privacy and data protection, as well as ongoing validation of AI models to ensure their accuracy and effectiveness.
How Can Fairo Help?
Healthcare is just one of many industries that are poised to make the most of AI advancements. Fairo is a SaaS platform focused on standards, simplicity, and governance to give organizations and their users across multiple industries the confidence to consume AI successfully and rapidly at scale. Fairo is committed to being the industry-standard platform for helping your organization implement its AI governance framework and strategy. Fairo seamlessly integrates into your existing ecosystem and is easy to consume.
AI is a disruptive technology that will change how people work and live. We envision a world where AI is universally built responsibly, trusted, and not feared. We aim to provide an easy-to-use solution that helps organizations procure, develop, and deploy trustworthy AI solutions with confidence.
