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How Companies Are Using Generative AI in Healthcare & Life Sciences

Humans have been making progress in biology for thousands of years, but those gains are often hard-fought and slow, with many false starts in between. With generative AI, healthcare and life sciences researchers have the potential to greatly accelerate what’s possible in scientific progress and human health.

Businesses recognize the transformative potential of generative AI. According to the Deloitte Center for Health Solutions, 75% of leading healthcare companies are either experimenting with or planning to scale generative AI. Efficiency and faster decision-making are among the top expected benefits. 

As healthcare and life science companies invest in generative AI tools, they’ll need to identify the most promising applications. We’re sharing nine use cases of generative AI in life sciences and healthcare that can promote patient care, improve efficiency, and drive innovation. 

The AI Opportunity in Healthcare and Life ‌Sciences

Healthcare is a deeply personal and sensitive topic. When it comes to personal well-being, we all want the best care possible. That’s why we place our trust in doctors, nurses, and other healthcare professionals with specialized expertise. Generative AI can be a powerful support system for the human element of healthcare while improving innovation, productivity, cost efficiency, and patient outcomes.

Healthcare and life sciences generate enormous quantities of data, including medical images, patient records, research papers, and clinical trial data. Generative AI can help organizations process and interpret that data faster and more effectively, giving researchers and practitioners valuable insights for R&D or patient care. These AI tools aren’t limited to text, either, as they can handle large data sets of images, video, and 3D models. 

As more data becomes available, large language models will learn and produce better diagnoses, treatment recommendations, and suggest scientific pathways. This ongoing learning process ensures that generative AI keeps up with frequent changes by incorporating the latest knowledge and insights into decision-making processes. It's like having an automated co-worker that learns alongside healthcare professionals.

9 Generative AI Use Cases in Healthcare and Life Sciences

Many organizations may be eager to incorporate generative AI into their systems and processes but are unsure where to begin. It can be helpful to start by learning different ways similar organizations in the same industry have found success with genAI. Let’s explore some common generative AI use cases in healthcare and life sciences.


Generative AI can be used to summarize vast amounts of data, including patient records and research. By leveraging advanced natural language processing (NLP) techniques, AI models can extract key information and provide concise summaries, highlights, and recommendations. 

This saves time and effort for researchers and healthcare professionals, and can even surface insights that humans might have overlooked. These professionals can rely on AI to help them stay current with the latest research, news, and techniques despite their busy schedules.

Semantic Search

Generative AI can power semantic ‌search capabilities in healthcare and life sciences. Instead of relying solely on keyword matching, AI models can understand the context and meaning behind the search query and provide more relevant results. This is particularly useful when searching for complex medical terms or finding information that’s relevant to your query but would otherwise be difficult or impossible to discover using traditional search methods.

Drug Discovery 

Generative AI models can assist in drug discovery research by analyzing vast amounts of data related to chemical structures, binding sites, and DNA sequences, as well as existing research and products. This can yield a list of potential drug candidates more quickly than asking researchers using less powerful software.

Generative AI can also be more thorough and wide-ranging in its search capabilities. For example, these tools can identify potential drug candidates that might be overlooked by human researchers, such as alternative uses for existing drugs. AI can also help with safety and testing by predicting how a drug might interact with specific genetic sequences. This capability can lead to faster, safer, and more effective treatments.

Better Communication 

Generative AI-enabled chatbots can improve communication between healthcare providers and patients. These chatbots can understand natural language and provide personalized responses, answer questions, schedule or change appointments, and provide basic medical advice. This improves accessibility to healthcare services, especially for patients who can’t easily travel. When AI-powered chatbots are combined with caring medical professionals, healthcare organizations will see higher patient engagement and satisfaction.

Chatbots can also help life sciences firms, giving researchers a trusted resource for searching for internal information and expertise. With AI embedded into knowledge management systems, R&D teams can spend less time searching for information and more time testing novel solutions.

Predictive Analysis 

GenAI-powered chatbots can also serve as valuable tools for medical professionals, helping them find new ways to manage diseases and diagnose conditions using predictive analysis and a conversational interface. Healthcare companies can create chatbots that help patients, too — analyzing patient data, genetic information and treatment history to generate personalized recommendations and insights.

For example, a chatbot powered by generative AI can examine a patient's medical history, symptoms, and genetic predispositions to predict the likelihood of developing certain diseases or complications. Based on this analysis, the chatbot can offer recommendations for preventive measures, lifestyle changes, or treatment options.

By preventing medical problems or catching them early, this proactive approach can improve patient outcomes and reduce healthcare costs.

Improved Patient Triage

Many healthcare organizations struggle with too many patients and not enough medical professionals. This requires them to triage patient care, but this task, too, can be intensive and stressful. 

Generative AI tools can assist in triaging patients — and ultimately improving care — by analyzing patient data, symptoms, and medical history. These AI models can additionally help healthcare professionals with diagnosis, treatment recommendations, and setting up care plans based on their condition and its severity. 

Better Patient Record-Keeping and Information Management 

Generative AI can streamline the management of medical records and other information without compromising patient privacy. By automating data entry, extracting relevant information, and ensuring data accuracy, AI tools can reduce administrative burden and improve record-keeping accuracy and efficiency. 

With generative AI and connected systems, medical providers can instantly access comprehensive and up-to-date patient information for better decision-making and coordinated care delivery. 

Increased Patient Personalization 

Generative AI can contribute to increased patient satisfaction by personalizing healthcare experiences. AI-powered applications and wearable devices can monitor patient health metrics, provide real-time feedback, and offer personalized recommendations for healthy living. This helps patients feel like they’re personally cared for and encourages them to participate in decisions about their care.

Streamlined Training and Certifications 

Generative AI can streamline training and certification processes in healthcare and life sciences. AI models can recommend certifications, courses, and training materials, ensuring that medical professionals focus on the professional development that matters most for their job role. 

Generative AI tools could even help produce training content, helping HR teams keep up with workforce learning and upskilling while still handling all their other responsibilities.

Generative AI for Healthcare & Life Science Companies (1)4 Risks to Consider When Using Generative AI 

Generative AI comes with inherent risks that must be mitigated to protect employee and patient privacy, meet compliance standards, and avoid legal and reputational risk. Healthcare data, in particular, is both highly sensitive and highly regulated. Without the proper IT and security infrastructure, compliance training, and ethical guidelines, your organization isn’t ready to implement generative AI to its fullest.

While no system is entirely foolproof, the industry has implemented compliance standards and architectural solutions to help keep data secure. Proactively educating teams about the risks and providing an opportunity to ask questions about generative AI is crucial. Here are some key topics to include.

Data Breaches and Unauthorized Access

The sensitive nature of health information makes it a prime target for malicious actors. A breach in data security can lead to the exposure of confidential patient information, compromising privacy and potentially harming individuals and healthcare organizations. Robust security measures and encryption protocols must be in place to protect against these risks.

While many types of cyberattacks are possible for healthcare and life sciences organizations, they must also be aware of the human factor. Many security breaches occur due to human error or employees falling victim to social engineering — a tactic where individuals are manipulated into providing access to confidential information. 

Additionally, make sure there are strong rules in place, including access controls. This prevents employees from accidentally leaking key information or departing the organization with intellectual property, access privileges, or other critical information. 

If not properly managed, employees and former employees can inadvertently or deliberately expose sensitive information to unauthorized third parties.

Compliance With Privacy Regulations

The healthcare sector is subject to stringent privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA). Compliance with HIPAA and other regulations helps protect patient privacy, and it starts with the secure handling of personal data. 

Generative AI systems must adhere to these regulations. Before adopting AI tools, make sure they meet security standards, have robust security measures, and don’t place claims on user data, including patient records and other sensitive information. 

Ethical Considerations

Generative AI has raised ethical concerns around copyright and intellectual property, as well as how these tools collect, store, and use data. Make sure your organization develops ethical policies that respect patient autonomy, consent, and confidentiality — both among your employees and in the technology you use. 

Transparent communication and informed consent processes are vital to maintaining trust between patients, healthcare providers, and AI systems. Make sure staff are trained in how to ethically use AI tools and the information generated by them.

Bias and Discrimination

Generative AI systems are trained on large datasets that may inadvertently contain biases and discriminatory patterns. If not carefully addressed, these biases can perpetuate inequalities in healthcare and life sciences. It's crucial to develop and implement robust mechanisms to detect and mitigate bias in generative AI models.. Regular audits and evaluations should be conducted to identify biases and rectify them.

Generative AI for Healthcare & Life Science Companies (2)Find the Generative AI Use Cases for Your Organization

Generative AI in healthcare and life sciences is a fast-evolving field, with nearly unlimited potential but also many questions about where to begin. As the technology evolves, expect new use cases to emerge and existing uses to improve. This, in turn, should help healthcare providers and life sciences companies win public trust for their use of AI in patient care and R&D, respectively.

Generative AI relies on trust as much as technology. Companies need confidence in AI’s performance, reliability, and lack of bias as they make business decisions and pursue innovation. Trust also matters when it comes to choosing a proven generative AI partner. Mission Cloud offers a wealth of experience and technical knowledge of data, analytics and machine learning, including generative AI. Find support for any AI project, whether it’s deploying generative AI models for automation or analysis or using it to create new content on the fly. 

Get in touch with a cloud advisor today.


Author Spotlight:

Ryan Ries

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