Why healthcare is betting big on generative AI
The power of gen AI to analyze vast datasets, from patient records and medical imaging to clinical trial results and research literature, is fueling its fast-growing adoption in healthcare. According to a McKinsey survey, 85% of healthcare organizations are either exploring or have already adopted gen AI capabilities.
By identifying patterns and generating predictions, LLMs can personalize treatment plans, improve diagnostic accuracy, and optimize administrative processes. In an industry that’s grappling with rising costs, burnout, and labor shortages — forecasting a shortage of over 85,000 physicians by 2036 — AI-powered solutions appear as a promising alternative.
A Harris Poll and Google Cloud research found that 90% of healthcare workers believe gen AI can alleviate administrative burdens across areas such as billing, documentation, pre-authorization, scheduling, inventory management, and more, freeing them to focus on delivering compassionate patient care. This desire for more direct engagement resonates with patients, as 85% in the same study expressed they would prefer their healthcare providers to dedicate more time to direct interaction and focused care rather than administrative tasks.
Gen AI use cases transforming healthcare today
Where is gen AI poised to make the most significant impact in healthcare?
A McKinsey report highlights administrative efficiency as the area with the most perceived potential (75%), closely followed by clinical productivity (74%), which encompasses use cases such as supporting prior authorization and translating unstructured notes into electronic medical records.
Let’s take a closer look at some of the core use cases of gen AI in healthcare:
Structuring and analyzing patient data
Patient data often comes in messy, unstructured formats. Think of free-text notes, PDFs, and imaging data. Generative AI models can parse, categorize, and summarize this data at scale, helping clinicians access key information faster.
For instance, HCA Healthcare’s partnership with Google has yielded a gen AI-powered technology designed to revolutionize bedside shift reports (also known as nurse handoffs), making them seamless and reducing administrative burden. The tool monitors a 12-hour shift and automatically generates a summary and a handover report for the incoming nurse, consolidating patients’ information such as vitals, allergies, significant events, and key conversations. In a pilot study, 90% of nurses found the model helpful enough to replace their current handoff documentation process.
Clinical decision support
Gen AI models can synthesize medical literature, patient histories, and real-time vitals to suggest possible diagnoses or treatment options. While clinicians remain the final decision-makers, AI helps surface insights they might otherwise miss.
A recent AI tool developed by Harvard Scientists has showcased remarkable capabilities in cancer diagnosis. Called CHIEF (Clinical Histopathology Imaging Evaluation Foundation), this technology can analyze digital tumor tissue slides to detect cancer cells with nearly 94% accuracy across 11 cancer types. Furthermore, CHIEF can predict a tumor’s molecular profile, forecast patient survival probabilities, and identify characteristics within the tumor’s surrounding environment that are relevant to treatment response.
A different use case shows AI’s potential in optimizing hospital discharge processes. By analyzing data such as patient history, labs, clinical notes, and risk factors, AI algorithms can predict optimal discharge times. This can help hospitals run smoothly, leading to reduced hospital stays, better allocation of resources, and improved patient flow.
Administrative automation
Routine tasks like charge capture, billing, and medical documentation take valuable time away from patient care. Gen AI can automate and summarize these workflows, improving efficiency while reducing burnout.
Auto-generated radiology summaries, for example, allow radiologists to focus on complex cases and speed up report turnaround, especially for high-volume procedures like X-rays and MRIs. This improves department efficiency and patient wait times. Similarly, chatbot-driven appointment scheduling can decrease no-shows by up to 30%, optimizing resource allocation, and improving patient experience, by reducing waiting times by as much as 80%. Furthermore, hospitals are integrating gen AI into Electronic Health Record (EHR) systems to streamline resource planning and triage.
High-quality data, essential for accurate gen AI in healthcare
At the heart of all these AI innovations lies one essential ingredient: high-quality training data. Gen AI models, especially in a sensitive field like healthcare, demand being trained on annotated data that is not only accurate but also contextually rich, supervised by domain-specific experts, and ethically sourced.
That’s where Sigma comes in. With a human-first approach to data annotation, we ensure that every data point — whether it’s a clinical note, patient symptom description, or medical image — is reviewed by trained experts. Tapping on our multilingual workforce of over 25,000 annotators, paired with detailed annotation guidelines, and continuous feedback loops, we are able to deliver consistent, representative, and unbiased training data.
Sigma blends human expertise, strong processes, and advanced AI tools to create the gold-standard datasets essential for building trustworthy and effective gen AI models in healthcare.
What are the challenges of gen AI in healthcare? (And how to overcome them)
Despite its vast potential, the widespread adoption of gen AI in healthcare still faces real challenges. Key barriers include a lack of technical expertise, insufficient data governance, and ongoing concerns around regulatory compliance.
Here’s how to address them:
- Privacy and security: Ensure all healthcare datasets are compliant with GDPR, HIPAA, and other local regulations. Sigma’s ISO 27001-certified processes and secure facilities help protect sensitive data at every stage.
- Data confidentiality: Leveraging synthetic data — artificially generated data that mimics the statistical properties of real patient data without containing any actual identifiable information — can mitigate security risks associated with using real patient data.
- Domain relevance: Avoid generalist AI. Use domain-specific data annotation teams to align data with real clinical workflows. Working with subject matter experts ensures training data that’s accurate and reliable, while preventing AI bias.
- Scalability: Work with a partner that can scale data annotation without compromising quality. At Sigma, we deliver at enterprise scale with native-language specialists across 100+ countries. Our rigorous staffing selection and training process ensures we identify the most qualified professionals for each project, assessing both relevant background and specific gen AI skills through targeted testing.
What’s next for gen AI in healthcare?
Gen AI is already making waves in the healthcare industry and its use will only grow. Near-future applications, some of which are already in the market, include:
- Real-time scribing during doctor-patient visits. AI-powered note-taking apps automatically transcribe and structure medical conversations into organized notes. This reduces physician documentation time and frees them to focus more on patient care.
- Generating synthetic data to support rare disease research. Addressing the challenge of limited patient data for rare conditions, artificial datasets created by gen AI will empower researchers to overcome key barriers in their pursuit of diagnostics and treatments.
- Supporting telemedicine with multilingual, AI-powered chatbots. AI chatbots can handle initial consultations, gather patient information, schedule appointments, provide pre and post-care instructions, answer frequently asked questions in multiple languages, and even offer basic symptom triage.
- Personalized treatment planning based on predictive models. Gen AI can analyze vast datasets of patient information, including genomics, lifestyle factors, and treatment responses, to create highly individualized treatment plans and predict patient outcomes with greater accuracy. For instance, Sigma has recently collaborated on a European project for developing smart, non-invasive monitoring solutions for chronic diabetes patients, particularly those with Type 2 diabetes.
Gen AI can transform healthcare, opening unprecedented opportunities. But getting it right requires precise data, ethical oversight, and close collaboration between humans and machines. Sigma AI brings 17+ years of experience in data annotation and a proven track record in sensitive, high-stakes domains like healthcare.
Partner with us to build your successful gen AI healthcare solutions. Póngase en contacto con nosotros to explore how we can support your data annotation strategy.