GenAI in Healthcare: Transforming Diagnostics and Patient Care

Learn how generative AI in healthcare is enhancing diagnostics, personalizing treatment plans, and improving operational efficiencies.

Less than one year after the initial launch of generative AI (GenAI), McKinsey called 2023 “generative AI’s breakout year.” Instead of traditional data analysis and automation, GenAI is more creative and multi-dimensional. GenAI in healthcare could reduce operational costs for the industry while improving patient care quality, accelerating drug discovery, optimizing supply chains, and improving job satisfaction for healthcare workers.

Today, the AI in the healthcare landscape is becoming more nuanced. In addition to the familiar large language models (LLMs), which excel at complex reasoning and broad knowledge tasks, we now have small language models (SLMs) to consider. SLMs offer distinct advantages for more focused healthcare workflows, including faster processing, lower computational costs, enhanced data privacy through local deployment, and specialized performance on domain-specific tasks.

With major challenges like staff shortages, stringent compliance and data standards, worker burnout, and high operational costs, LLMs and SLMs will be pivotal in optimizing resources over the next decade. Healthcare costs are predicted to increase by eight percent in 2025, and almost 33 percent of healthcare employees are experiencing one symptom of burnout. By 2030, the World Health Organization predicts a large shortfall of 10 million health workers, especially in lower socioeconomic countries.

Benefits of GenAI for Healthcare Professionals and Patients

Compared to traditional AI capabilities, generative AI technology is a significant leap forward in creativity, personalization and scalability. Benefits for healthcare include enhanced diagnostic accuracy and speed, more personalized treatment plans, better patient outcomes, and, ultimately, increased trust between patients and providers.

1. Enhanced Diagnostic Accuracy and Speed

GenAI in healthcare can rapidly analyze huge sets of medical data, including patient information and genomic details, to improve diagnostic accuracy and speed, as well as medical images like X-rays, MRIs, and CT scans. GenAI can detect subtle changes, patterns, and early indicators that lead to potentially life-saving detection. GenAI in healthcare can also support human physician work by generating potential illnesses and less common conditions for a more comprehensive diagnosis.

SLMs, meanwhile, can help with routine diagnostic tasks such as focused image analysis that can identify specific pathologies in chest X-rays or detect diabetic retinopathy in retinal scans. Healthcare organizations can deploy specialized models directly on medical devices or local servers, providing real-time analysis without LLMs’ cloud connectivity requirements or considerable computational overhead.

2. Improved Patient Care and Personalized Treatment Options

Ultimately, faster and more accurate detection and diagnosis help improve patient outcomes, reduce illness severity, and foster a better patient experience with more personalized treatment. An improved patient experience with GenAI means providers have more time to spend with patients answering questions and discussing treatment versus spending hours on charting or diagnostics.

Patient-facing applications such as symptom checkers, medication reminders, and basic health coaching chatbots are increasingly important for patients, and they are perfect for SMLs. Their smaller size enables deployment on mobile devices and wearables, allowing for personalized health monitoring and intervention without draining battery life or requiring constant internet connectivity.

3. Operational Efficiencies and Cost Reductions in Healthcare

As healthcare costs rise across the board thanks to inflation, increased prescription drug spending, and increased care needs, the healthcare industry faces urgent pressure to improve operational efficiencies and reduce costs. Technological investments like GenAI are one way to reduce overhead, automate manual tasks, and expedite drug development and trials.

For example, SLMs can complete high-volume, repetitive tasks with lower computational costs and faster response times. Those capabilities drive out administrative costs tasks such as appointment scheduling, insurance preauthorization processing, and basic medical coding.

While the benefits are extensive, like any industry, healthcare faces serious challenges and ethical considerations regarding data privacy, security issues, and potential biases in AI algorithms.

Challenges and Ethical Considerations of Implementing GenAI in Healthcare

Cybersecurity and potential damaging bias are two of the top concerns and challenges regarding large-scale AI usage. Not only are patients concerned about the privacy of personal medical information, but potentially, incorrectly trained algorithms could lead to discriminatory treatment.

1. Data Privacy and Security Concerns

Healthcare data is extremely sensitive and 10 times more valuable on the black market than financial data. The average cost of a healthcare data breach is nearly $10M, making it the most expensive type of data breach in any industry. Healthcare cyberattacks are extremely lucrative for cybercriminals, leading to a major risk of data breaches and exposure. With LLM GenAI aggregating and storing huge amounts of healthcare data, consumers and providers are concerned about the consequences of misuse or exposure.

In 2024, the largest healthcare data breach at Change Healthcare exposed 100 million patient records and cost the organization a $22 million ransom. Genetic firm 23andMe was also hacked, exposing family genealogical trees, medical data, and locations of 6.9 million people. Unfortunately, healthcare data breaches are increasing, and GenAI may add more fuel to the fire.

2. Bias and Fairness in AI Algorithms

The nature of large AI models makes them susceptible to the information on which they’re trained. When discriminatory data is baked into learning, the AI tool becomes even more skewed and biased. Plus, it’s not an easy solution to train every AI model on perfectly fair data, as many racial and gender biases exist in the real world.

For example, during the COVID-19 outbreak, an AI algorithm was trained on biased data that inadvertently deprioritized people of color. Underrepresented people might make up a smaller proportion of the learning data, leading AI models to have lower accuracy for black patients vs. white patients.

SLMs can address this challenge because they are easily fine-tuned on specific, curated datasets to address bias concerns. For instance, an SLM focused on dermatological conditions can be trained on diverse skin tone datasets, or a model for cardiac risk assessment can be optimized to perform equitably across different demographic groups.

Strategies for Ethical AI Implementation in Healthcare

There are also real-life solutions on the horizon for ethical AI implementation. AI ethics leaders call for diverse, proportional training on algorithms and human oversight for potential corrections.

Healthcare leaders should create and implement ethical AI governance policies to ensure consistent compliance, enhanced consumer trust and transparency, and, most importantly, fairness, equity, and inclusion. For example, healthcare leaders could establish an AI ethics committee and proactively collaborate with governmental bodies working on large-scale ethics frameworks.

Now that we understand both the benefits and potential challenges of implementing LLM and SLM generative AI in healthcare, let’s take a look at some real-life applications.

Real-Life Applications of Generative AI in Healthcare

From cancer to Alzheimer’s to diabetes and elderly care, generative AI is already transforming many different healthcare industries. These real-life applications are more than simply experimental innovations. Real benefits include improved disease diagnosis, better patient care, speedier drug development, and more personalized treatment plans. Top generative AI in healthcare applications involves medical imaging analysis, algorithms for predictive learning, and pattern recognition for treatment plans.

Cancer Treatment

For example, a new GenAI tool forecasts patient survival and recommends treatment plans based on medical imaging of cancerous tumors. With AI analysis of lung CT scans, doctors can more accurately predict the likelihood of a patient developing cancer within six years.

At a more granular level, SLMs can be directly integrated into imaging equipment to provide immediate results for cancer or other patients. This reduces the need for follow-up appointments while maintaining high accuracy rates for specific cancer types.

Drug Discovery

At Insilico Medicine, a proprietary GenAI tool designed drug INS018_055 to treat a rare lung disease. It’s one of the first AI-created drugs to reach advanced stages of clinical trials, highlighting a major breakthrough in analyzing huge data sets of molecular information and subsequent drug creation.

Personalized Medicine and Treatment Plans

Another AI tool predicts a patient’s response to cancer treatment, allowing providers to substitute and adjust medicines, chemotherapy and more for better outcomes. Patients receive more accurate and personalized treatment plans, potentially improving quality of life and survival rates.

SLMs can take the next step by creating personalized treatment protocols for chronic conditions such as diabetes and hypertension. They can also process individual patient data patterns, such as medication adherence, generate customized care plans, medication timing recommendations, and lifestyle modifications.

These real-life applications are exciting and innovative, highlighting many use cases across the entire healthcare industry in the coming decades.

Future Prospects of GenAI in Healthcare

AI in healthcare has already seen giant leaps forward and huge benefits. The global AI healthcare market is expected to grow 49.1 percent, reaching $164 billion by 2030 due to the rise in chronic disease, an aging population, and a healthcare worker shortage. New technologies like AI-powered surgical robots, more effective algorithms, and even communication systems for more transparent, collaborative care will continue to emerge.

The future of healthcare AI will likely feature a hybrid approach, with SLMs handling routine, specialized tasks, and LLMs tackling complex diagnostic reasoning and comprehensive treatment planning. This complementary approach will optimize both cost-effectiveness and clinical outcomes across healthcare systems.

Generative AI Will Shape the Next Decade of Healthcare

Whether through LLMs or SLMs, generative AI in healthcare does more than just clean up data. It uses machine learning algorithms to analyze unstructured data and produce new content, leading to a wide variety of innovations in healthcare. From more accurate predictions of tumor growth to custom Alzheimer’s treatment plans, the applications are limitless when organizations solve some of the data security and ethical concerns around AI usage.

Don’t get left behind in the AI revolution. We guide leaders through the disruption AI may cause to help you go from uncertain to excited about the potential of using AI. Ready to get started? Contact Us