Generative AI is making huge strides in all industries. Healthcare is one of those industries that will see phenomenal impact of this technology. Here is the list of some of the Healthcare use-cases and the challenges that come as side-effect of using Generative AI.
Medical Conversation Summaries : Abridge has built a platform that uses generative AI to create summaries of medical conversations from recorded audio during patient visits. That helps doctors cut down on the amount of time they spend on notes, which can add up to over two hours a day.
Synthetic Data Generation : Startups like Syntegra are using generative AI to create so-called synthetic data, or fake versions of patient records that maintain the properties of the original. Syntegra’s technology is being tested by Janssen Pharmaceutical Cos., a drug company owned by healthcare giant Johnson & Johnson.
Streamlined drug discovery and development: Generative AI can help speed up the process of drug discovery and development by identifying potential drug candidates and testing their effectiveness in silico (i.e. using computer simulations) before moving on to the clinical trials on animals and humans. For example, Newly synthesized drugs must be tested on animals or humans before launching into the market. However, there is always a risk involved with drug testing on animals or humans. Generative AI can help identify the right candidates for drug testing based on their bodily functions. It can also run simulated tests on different candidates in synthetic environments. When the chances of success are high, a real test is performed on the selected candidates.
Personalized medicine: Generative AI algorithms can potentially help create personalized treatment plans for patients by taking into account their medical history, symptoms, and other factors. To further explain this, a custom treatment plan is prepared according to the medical history of a patient. The bodily functions of a patient are also considered for creating a custom medical plan. However, healthcare entities fail to create custom treatment plans for each patient. Since the healthcare sector deals with many patients daily, it is a challenge to create a personalized treatment plan for each patient. Generative AI can take the medical history, symptoms, and bodily functions of the patient as input to generate custom treatment plans. Healthcare entities do not have to worry about the service availability of a software system that generates treatment plans. Since generative AI can produce custom treatment plans within seconds, there is no need for an external software system.
Improved medical imaging: Generative AI can help improve the accuracy and efficiency of using machine learning in combination with medical imaging techniques, such as CT and MRI scans. Machine learning models can automatically identify abnormalities in images and alert doctors to potential issues.
Health Management and healthcare Initiatives: Generative AI can analyze large amounts of data within seconds. Healthcare entities have to focus on population health management to launch specific services. For example, healthcare entities might deploy new treatment techniques for people in an area known to possess hereditary diseases. Generative AI can analyze demographic information on a granular level and generate rich insights. If healthcare entities are looking to launch healthcare schemes for some areas or unrecognized communities, generative AI is the right choice. Even government healthcare organizations can use generative AI for better population health management. Governments are expected to invest heavily in generative AI for launching effective public healthcare schemes.
Medical Research Understanding : Generate reports or summaries of medical research articles for easier interpretation by clinicians. This also includes summarization of scientific research articles for easier interpretation by researchers and for relevant citations.
Preventive Care : Generate recommendations for preventive care based on an individual’s risk factors, lab results and medical history.
While these are some of the use-cases, Generative AI brings its own set of challenges that get more pronounced in Healthcare industry. Some of these challenges are -
Privacy and security: Patient privacy is strictly regulated. The use of generative AI in healthcare also raises concerns about protecting patient privacy, sensitive medical data and the potential for misuse or unauthorized access to the healthcare data.
Bias and discrimination: Generative AI algorithms can be prone to bias and discrimination, especially if they are trained on healthcare data that is not representative of the population they are intended to serve. This can result in unfair or inaccurate medical diagnoses or treatment plans for underprivileged groups such as women or non-white races.
Misuse and over-reliance: If generative AI algorithms are not used properly, they can lead to incorrect or harmful medical decisions. In addition, there is a risk that healthcare providers may become overly reliant on these algorithms and lose the ability to make independent judgments.
Ethical considerations: The use of generative AI in healthcare raises several ethical concerns, such as the potential impact on employment in the healthcare sector.
Generative AI has the potential to significantly impact the workplace by automating routine tasks, personalizing products and services, and enhancing creativity. It can also facilitate collaboration between humans and machines and create new revenue streams and market opportunities. However, it can be hard to imagine where Generative AI will take us, including the impact on the future of work, trust, and human-machine interaction. Experts believe that generative AI will soon enter workplaces and transform established professions while giving rise to new ones. With ethical and thoughtful deployment, it is a tool that could help precipitate a revolution in creativity — one that enables everyone to better express their humanity.