Generative AI in Healthcare
Generative AI (GenAI) has the potential to transform clinical workflows. Disparate sources of unstructured data, which abound in healthcare, are now assets to power GenAI. It represents a meaningful new tool that can help unlock a piece of the unrealized $1 trillion of improvement potential present in the industry. It can do so by automating tedious and error-prone operational work, bringing years of clinical data to a clinician’s fingertips in seconds, and by modernising health systems infrastructure.
I have previously looked at Generative AI in Education and recommend reading these two articles together to get better understanding of broader use cases in both sectors.
Clinical Notes App
GenAI can streamline and automate the clinical note taking process by capturing the key facts from patient conversations and summarising them as notes in Electronic Health Records. It can also be used to summarize and create clinical notes such as visit summaries, discharge notes, radiology reports, or pathology reports. The technology can also simplify complex medical language into summaries and translate them into any language so patients can understand easily. At the basic level, GenAI can help healthcare professionals interpret data such as a patient's medical history, imaging records, genomics, or laboratory results with a simple query, even if the information is stored across different formats and locations. For instance, if information about a cohort of female patients aged 45–55 years is required along with access to their mammograms and medical charts, a natural language query can be entered into the search tool instead of seeking out each element separately. Microsoft has launched Dragon Ambient eXperience (DAX) Express, an artificial intelligence-powered clinical notes app for healthcare professionals. DAX Express is an automated clinical documentation application integrated into the workflow that is the first to combine proven conversational and ambient AI with the advanced reasoning and natural language capabilities of OpenAI’s GPT-4.
Drug Discovery
AI is already more integrated into diagnostic algorithms for screening diseases such as cancer, cardiovascular diseases, and diabetes. Machine learning and deep learning algorithms are used in the analysis of data and patient history. The use of GenAI can accelerate the drug discovery and development process. By searching through the medical and scientific literature on websites, such as PubMed, potential drug candidates can be identified and tested for effectiveness using computer simulations (in silico) before proceeding to clinical trials on animals and humans. Furthermore, GenAI models can also accelerate drug development by generating novel molecular structures with desired properties. It is entirely conceivable that GenAI could eventually replace the need for most clinical trials and laboratory experiments, thus accelerating clinical and scientific developments at an unprecedented pace. GenAI can design clinical trials and author protocol documents. It can also create Clinical study reports by generating summaries of clinical trials, including the study design, patient characteristics, efficacy and safety results, and statistical analyses.
Patient Communication
GenAI based virtual assistants can help patients schedule appointments, receive treatment, and manage their health information. It can also be used to monitor patients remotely by analyzing data from wearables, sensors, and other monitoring devices, providing real-time insights into a patient’s health status to healthcare providers. Patients can communicate with GenAI using natural language and ask questions related to drugs, including dosage, side effects, and interactions. GenAI can also provide students and healthcare professionals with instant access to the latest research, guidelines, and practices, thus supporting their ongoing learning and development.
Outlook
This trend of leveraging the capabilities of large language models (LLMs) is likely to continue and grow exponentially, particularly in areas that don’t have a direct impact on patient health. Once GenAI matures, it could also converge with other emerging technologies, such as virtual and augmented reality or other forms of AI, to transform healthcare delivery. For example, a healthcare provider could license its likeness and voice to create a branded visual avatar with whom patients could interact. Or a healthcare professional could check, against the full corpus of a patient’s history, how their approach for that patient aligns (or deviates) from other similar patients who have experienced positive outcomes. These ideas may seem distant, but they have real potential in the near term as GenAI advances.
The global generative AI in healthcare market accounted for USD 0.8 billion in 2022. It is expected to reach a valuation of USD 17.2 billion with a CAGR of 37.0% by 2032. Asia Pacific is expected to show significant growth during the forecast period. The increasing technological advancements in emerging economies such as China, Japan, Singapore, and India are driving the regional growth of the market. In addition, the increasing need for big data and rapid growth in healthcare technology is expected to impact market growth positively.
Addressing GenAI Risks
GenAI may also potentially use patient and healthcare information to improve the training of its models. If the data sets used by GenAI are based on an overindex of certain patient populations, then a patient care plan generated may be biased, leaving patients with inaccurate, unhelpful, or potentially harmful information. Given the potential for GenAI to come up with potentially inaccurate answers, it will remain critical to keep a human in the loop. However, the ability of GenAI to generate synthetic medical data can address the these issues. Also GenAI generated synthetic data such as medical images that can be used to augment training data and create diverse datasets for research and medical training.
To weigh the value of GenAI applications in healthcare against the risks, leaders should create risk and legal frameworks that govern the use of gen AI in their organizations. Data security, bias and fairness, and regulatory compliance and accountability should all be considered as part of these frameworks. To help bring these changes to healthcare, organizations must learn how to use GenAI, evaluate recommendations, and intervene when the inevitable errors occur. In other words, AI should augment operations rather than replace them.