Generative AI in Healthcare: How it Can Help?
Healthcare costs are predicted to increase by 7% because of the increasing challenges in the healthcare industry. These challenges include burnout of healthcare staff, staffing shortages, and disagreements between payers and providers. However, it is fundamental for healthcare organizations to offer high-quality patient care with controlled expenses despite the issues the industry faces. In doing so, the healthcare industry focuses on examining the possibility of adopting new technologies, particularly generative artificial intelligence (AI). As per Accenture, generative AI in healthcare can enhance 40% of healthcare providers’ working hours.
Generative AI integrated in healthcare utilizes machine learning algorithms for unstructured data analyses, including patient health records, medical photos, and audio recordings of consultations. From diagnosis to patient care management, the advent of generative AI in the healthcare industry can be considered to have the potential to transform the well-being of people fundamentally. Let’s find out how generative AI and healthcare intersect.
The Power of Generative AI in Healthcare
Over the past year, large language models (LLMs) and generative AI have garnered attention from clinicians, biopharma, payers, and insurance providers. For a long time, the healthcare sector has been relying on classical analytical AI. However, generative AI in healthcare can come up with new content, translate the old content, and do reasoning and planning.
On the part of health care professionals and the course of providing care, the technology proposes a possibility of decreasing the amount of time necessary for each patient visit and settlements about payment. This, in turn, reduces staff absenteeism and motivates doctors and staff, all while cutting down administrative costs.
Generative AI for healthcare is speeding evolutionary research in biomedical and pharmaceutical domains. Molecular biology-themed LLMs can also be used to predict protein structure and binding affinity while simultaneously developing novel therapeutic candidates.
Insurance companies and payers are also making the most of the technology. It is used in data analytics, processing automation for claims management and fraud detection, and maintaining quality and occurrence of risk management.
Applications of Generative AI in Healthcare
Increased productivity, enhanced patient and provider experiences, and ultimately improved clinical outcomes are all potential outcomes of generative AI. The technology could accelerate biomedical research and drug development, reduce administrative expenses, enhance claims administration, and contribute to the creation of next-generation diagnostic equipment.
1. Task Automation
Research indicates that the burnout rate among physicians in the US has reached an astonishing 62%. Burned-out physicians are likely to put their patients in danger, as well as being more prone to alcohol addiction and experiencing suicidal ideation. However, generative AI in healthcare can help reduce doctors’ workload by automating administrative tasks. It decreases expenses related to administration, which makes up 15%-30% of total healthcare expenditure.
2. Personalized Health Advice
A new wave of virtual health assistants has been made possible by advanced GenAI models like GPT-4 and the knowledge of human doctors. Ada is an AI-powered app created by a doctor that can look at a patient’s symptoms and give them medical advice in different languages. Thirteen million people have used the app and have done more than 30 million symptom reports so far. It asks you about your symptoms (you can make different profiles for family and friends’ symptoms, too) and then suggests possible conditions and medical help. The app also keeps track of how your symptoms change over time. We can expect GenAI to help out with some of the medical care that millions of people around the world cannot get because of various circumstances.
3. Tailored Treatment Plans
Generative AI in healthcare helps physicians enhance patient treatment by analyzing vast patient datasets. The analysis aids in recommending personalized treatment plans, optimizing medication dosages, and predicting potential adverse reactions. Moreover, generative AI for healthcare also helps in producing customized rehab exercises and therapy that meet each person’s specific needs. It would further elevate the care system by enhancing preventive treatment. Clinics and hospitals can carry out specific health plans unique to each patient, considering genetic variations, health history, diet, and lifestyle factors.
4. Early Detection
Generative AI in healthcare serves an important role in image analysis and early detection of diseases. The apps are used for the diagnosis purposes of radiologists who identify and spot diseases quicker and more precisely by using X-rays, MRIs, and CT scans.
One study explored the use of AI to interpret chest radiographs and generate radiograph reports in the emergency department. Since many emergency departments don’t have 24/7 access to dedicated radiology services, images are often interpreted by a remote radiologist or even by ER doctors. The study found that the AI tool generated rapid radiograph interpretations and reports with comparable levels of quality and accuracy as radiologist reports – and to a higher quality than teleradiology reports. In one of the cases, the AI performed even better than a human radiologist, detecting an issue that the radiologist failed to report. This shows that not only can AI help radiologists perform their work more quickly and effectively – it can also help clinicians in other departments interpret medical images and accelerate the care process.
5. Drug Development
Generative AI technologies have been playing a crucial role in the drug discovery process. With the help of technology, researchers have learned to find the most affordable option for designing possible combinations of drugs (and even completely new ones) to form new pharmacological treatments. It will accelerate drug discovery and development by the creation of new molecular entities. It has been doing wonders with regard to high-throughput screening, prediction of drug-to-drug interactions, identification of existing drugs for conversion to novel prescriptions, optimization of clinical trials, and meta-formulation of drugs.
Conclusion
With the ongoing integration of technology in healthcare, GenAI is expected to play a leading role in this transition. Generative AI in healthcare can bring about significant breakthroughs in consumer engagement, patient care, and operational efficiencies. Healthcare organizations can ensure a future where GenAI enhances healthcare delivery in a fair, unbiased, trustworthy, and ethical manner while also maintaining a personal touch. AI has the potential to alleviate significant challenges in healthcare, such as the digitalization of medical records, increasing expenses, and administrative hassles. By responsibly implementing the technology, it is possible to overcome these obstacles. Exploring the possibilities of generative AI in healthcare can bring benefits to major stakeholders in the healthcare business, including personalized treatment, patient engagement, and operational optimization.
Also Read: The 6 Benefits of Artificial Intelligence in the Healthcare Industry