How to Navigate the Potentials of Generative AI in Healthcare

To address these issues and sustain healthcare organizations while improving patient care, healthcare organizations should embrace innovative technologies. Generative artificial intelligence (GenAI) stands out as a promising solution, offering enormous potential benefits for both enterprise-wide and direct-to-consumer applications.Recent advancements in natural language processing (NLP) or large language models and machine learning models have significantly impacted healthcare, but new Generative AI models are pushing the boundaries even further. These models excel in natural language generation, summarization, translation, insight retrieval, reasoning, handling unstructured data, and beyond. As a result, Generative AI has the power to democratize knowledge, enhance interoperability, speed up discovery, and enable true personalization in healthcare.It’s no surprise that the potential of Generative AI is already being recognized in the industry, according to the Deloitte Center for Health Solutions.

• 75% of leading healthcare companies are experimenting with or planning to expand Generative AI across their operations.
• Leaders anticipate that Generative AI will improve efficiency (92%) and accelerate decision-making (65%).

This article will guide you through the essentials of Generative AI in healthcare, exploring transformative applications, strategies for successful adoption, future trends, and more.

Let’s get started!

Top 5 Primary Applications of Generative AI in Healthcare

While Generative AI cannot replace doctors and nurses, it’s able to complement their skills with rapid insights and analysis. It can also reduce many routine, time-consuming tasks, allowing healthcare professionals to focus more on patient care and improving outcomes.

1. Virtual Health Assistants

After COVID-19, many healthcare providers have implemented remote consultation services, enabling patients to consult with doctors without needing to visit hospitals. Despite being effective, this approach often overburdens physicians who have to manage both online and offline patients.

Generative AI comes in handy in this case, offering AI-powered virtual assistants.

These virtual healthcare chatbots provide patients with easy access to medical services through conversational interactions. Integrated with major electronic health record systems, they let users schedule, reschedule, join waitlists, or cancel appointments without human intervention.

AI virtual assistants also send personalized medication reminders and adherence alerts to patients, supporting medication compliance by sending timely notifications via mobile apps or smart devices. This function significantly reduces the risk of treatment interruptions and adverse outcomes.

Health organizations can further improve these assistants by fine-tuning foundation models like GPT-4 and Claude with healthcare data. Such assistants can manage basic medical cases and guide patients to appropriate treatments based on their symptoms. For more complex cases, the model can refer patients to the nearest healthcare professional, ensuring all cases are addressed without overwhelming doctors.

Many companies, such as Sanofi, Bayer, and Novartis, have successfully implemented GenAI-fueled assistants on their platforms.

One notable example is Ada, an AI-driven app designed to assess symptoms and offer medical guidance in multiple languages, from English, German, French, Spanish, and Portuguese to Swahili. With 13 million users and over 30 million symptom assessments, Ada asks users about their symptoms, creates profiles for loved ones then suggests possible conditions and medical advice, in addition to tracking symptom progression over time.

2. Drug Discovery

Developing a new drug typically takes 12 to 18 years and costs an average of $2.6 billion. However, only 10% of drug candidates reach clinical trials.

Generative AI holds significant potential to speed up and enhance this process. While most applications of Generative AI focus on creating new drug molecules (de novo drug design), the technology is also promising for target identification and drug repurposing.

Generative AI can analyze complex medical data to identify unique disease markers and propose new chemical combinations or novel molecular structures as potential drug candidates. This technology is also able to screen generated compounds for their properties, predicting side effects and drug interactions.

Several companies have developed Generative AI platforms for drug discovery, including Insilico Medicine, Exscientia, Iktos, Adaptyv Bio, and AbSci.

Notably, Insilico Medicine developed the world’s first AI-generated anti-fibrotic small molecule inhibitor, INS018_055, for treating idiopathic pulmonary fibrosis. This drug has successfully completed Phase 0 and Phase I trials and entered Phase II clinical trials in June 2023, moving closer to wider release.

Besides, Insilico Medicine utilized GenAI to create ISM6331, a molecule aimed at targeting advanced solid tumors. The medicine has shown promise as a pan-TEAD inhibitor, blocking proteins crucial for tumor growth and drug resistance. Early preclinical studies suggest that ISM6331 is both efficient and safe for use.

3. Medical Imaging and Diagnostics

AI has already made significant strides in healthcare, especially regarding diagnostics, but Generative AI is set to further revolutionize medical imaging.

Generative AI technology is increasingly used to assist radiologists in accurately and quickly identifying diseases from X-rays, MRIs, and CT scans. These generative AI models trained on extensive patient data can detect early signs of various health conditions, such as skin and lung cancers, Alzheimer’s disease, and diabetic retinopathy.

These healthcare systems work by uncovering biomarkers for specific disorders and predicting disease progression. Thus, they accelerate the diagnostic process and enhance accuracy to detect earlier and improve patient outcomes.

According to the Harvard Business Review, GenAI applications are estimated to reduce diagnostic errors by 85%, showcasing the significant ROI that Generative AI brings to healthcare diagnostics.

4. Streamlined Healthcare Operations

Generative AI applications do a good job of alleviating administrative burdens in healthcare settings. According to Accenture, AI could potentially enhance the efficiency of up to 40% of healthcare providers’ working hours.

Generative AI excels at streamlining administrative tasks, from transcribing and summarizing patient consultations to filling electronic health record (EHR) fields and generating clinical documentation. It also optimizes appointment scheduling by analyzing patient needs and doctor availability and creates personalized appointment reminders and follow-up emails.

Additionally, GenAI can review medical insurance claims to predict rejections and compose patient feedback surveys, analyze responses, handle billing, and provide insights to improve healthcare delivery.

A study published in Mayo Clinic Proceedings showed how surgeons used a large language model (LLM) to produce high-quality clinical notes in seconds, a task that typically takes seven minutes. This represents an 84-fold increase in efficiency, benefiting both patients and physicians.

5. Medical Education and Training

Generative AI presents a powerful tool for medical education and training, offering a versatile platform to generate virtual patient cases. These cases can encompass a wide range of medical conditions, patient demographics, and clinical scenarios, providing a comprehensive learning experience for medical students and healthcare professionals.

One key advantage of using Generative AI in medical education is creating a safe and controlled environment for learning. Students can interact with virtual patients, make diagnoses based on such data, and propose treatment plans without any risk to real patients.

Generative AI tools also simulate rare and complex cases, giving students valuable experience that they might not encounter often in clinical practice. This exposure prepares them for unexpected situations and enhances their problem-solving abilities. Personalized learning is another huge perk, as generative AI adapts to each student’s pace and needs.

Furthermore, students are able to improve communication skills with generative AI. To do so, they simulate patient interactions, teaching them to deliver difficult news empathetically.

Learn more: Applications of Generative AI in Education

4 Key Components of Successful Generative AI Adoption in Healthcare Organizations

In this section, we explore four essential elements that health systems should consider when successfully integrating Generative AI into their services.

1. Acceptance and Adoption

For Generative AI to be successfully implemented in the healthcare industry, it’s crucial that both medical professionals and patients understand and accept its applications. This understanding builds trust in GenAI systems, facilitates their effective use, and helps navigate ethical and regulatory challenges.

Five crucial elements that businesses should align with to ensure acceptance and adoption are:

• Perceived Usefulness: This factor refers to how much individuals believe generative AI will improve their job performance. In healthcare, this could mean how generative AI applications diagnose diseases, predict outcomes, personalize treatments, or automate routine tasks.
• Perceived Ease of Use: How easy it is for users to interact with GenAI healthcare systems. This includes the simplicity of the user interface, the clarity of GenAI outputs, and the level of technical support available.
• Attitude Towards Using: The benefits of Generative AI, such as cost-effectiveness and personalized health care, can positively influence users’ attitudes. Adequate training, support, and showcasing successful implementations can further enhance this positive outlook.
• Behavioral Intention to Use: A positive attitude leads to a higher likelihood of intending to use generative AI. This intention can be translated into actual use if we integrate GenAI into existing workflows and provide a supportive environment for its implementation.
• Actual System Use: The final step is the daily use of Generative AI in healthcare practice. Some measures to encourage ongoing use are continuous support and monitoring, along with feedback-based improvements.

It’s worth noting that external factors like regulatory approval, ethical considerations, patient acceptance, and the overall healthcare policy environment also influence the adoption of such a new technology as GenAI.

2. Data and Resources

Adopting Generative AI requires careful preparation of data and resources, which cover key steps as follows:

• Identifying Use Cases: Determine where Generative AI can add value, such as diagnosing chronic or acute illnesses. The sophistication of the GenAI models depends on the medical condition’s complexity.
• Data Collection: Gather clean, high-quality, and relevant data from existing data sources, new collections, etc., to train generative AI models.
• Data Cleaning and Preprocessing: Clean and preprocess the data to guarantee quality and consistency. This requires removing duplicates, handling missing values, standardizing formats, and enhancing quality via normalization, data augmentation, etc.
• Data Annotation and Labeling: Annotate and label the data according to clinical standards for accurate training later on. This may necessitate image segmentation or text categorization.
• Data Storage and Management: Set up a robust system for storing and managing large data volumes, whether through data warehouses, cloud storage, or data management platforms. Data federation technology is in charge of integrating data from various sources while keeping it within its respective firewalls. Here, data security and accessibility are top priorities.
• Computational Resources: Generative AI models often need substantial computational power, like GPUs and cloud services. Organizations must take these requirements into account, especially if they aim to develop proprietary models. To alleviate some of this burden, using commercial large language models (LLMs) through APIs or cloud services is highly recommended.

3. Technical Integration

To integrate Generative AI tools into a healthcare system, medical facilities should follow a detailed process outlined below, from identifying system needs to maintaining GenAI solutions:

• Step 1: Identify Focus Area: Determine specific applications of Generative AI, such as improving patient care, streamlining administrative tasks, enhancing diagnostic accuracy, and predicting patient outcomes.
• Step 2: Select the Right Model: Choose an appropriate GenAI model based on the identified applications. For example, Generative Adversarial Networks (GANs) can generate synthetic medical images or augment medical datasets, while LLMs can analyze electronic health records (EHRs) and support clinical decisions.
• Step 3: Train the Model: Feed data into the chosen model and adjust its parameters to ensure accurate predictions and other kinds of outputs.
• Step 4: Integrate with Existing Systems: Develop an interface or API to connect the Generative AI model with the current healthcare information system, allowing for smooth data access and output utilization.
• Step 5: Test and Validate: Thoroughly test the GenAI system for functionality, usability, and reliability.
• Step 6: Maintain and Update: Regularly update the GenAI model with new data and retrain it as needed. Collect feedback from healthcare leaders to make necessary refinements.

Additionally, it’s important for organizations to align their GenAI systems with relevant healthcare regulations and privacy laws, such as the Health Insurance Portability and Accountability Act (HIPAA) in the USA and the General Data Protection Regulation (GDPR) in the European Union.

 4. Governance

The last component is strict governance that aims to address different challenges of the Generative AI implementation process:

• Data Availability: Generative AI relies on large datasets for training, but clinical data is often limited due to privacy regulations, which can hinder effective model training.
• Bias in Training Data: If the training data is biased, the GenAI model will produce biased results. For instance, data skewed towards a particular demographic can lead to inaccurate outcomes for other groups.
• Transparency: While advanced language models like ChatGPT can produce impressive conversations, the lack of transparency about their training data sources poses legal and ethical issues.
• Model Interpretability: Generative AI models’ complexity makes it challenging for healthcare providers to understand how the models reach their conclusions, which can undermine trust and prevent integration into clinical workflows.
• Inaccurate Generation: LLMs can generate fluent text but may produce misleading or incorrect information in critical healthcare scenarios, causing significant risks to patient safety.
• Regulatory and Ethical Issues: Generative AI applications in healthcare must navigate complex regulatory and ethical landscapes concerning sensitive patient information, data ownership, and accountability, making it necessary to adhere to fundamental regulations like GDPR and HIPAA.
• Validation: To make sure generative AI models are accurate and reliable, you need extensive datasets and rigorous testing. Still, these processes are known to be both time-consuming and expensive.

Steps to Overcome Governance Challenges

• Establish a Governance Committee: Form a committee comprising experts in AI, healthcare, ethics, law, and patient advocacy to oversee the development and deployment of Generative AI use cases in business, confirming GenAI solutions meet ethical, legal, and professional standards.
• Develop Policies and Guidelines: Create comprehensive policies addressing patient data protection, security, informed consent, transparency, accountability, and fairness. Include protocols to prevent GenAI misuse and establish clear reporting and resolution procedures.
• Implement Robust Data Management Practices: Warrant data privacy and security, obtain informed consent for data use, and maintain high data quality and integrity. Use diverse and representative datasets to minimize bias in GenAI outputs.
• Mitigate Inaccurate Generated Data: Conduct thorough testing on large, validated datasets, provide transparent uncertainty quantification, and encourage multi-modal human-AI collaboration with consistent expert oversight to prevent errors.
• Conduct Risk Assessments: Carry out structured risk assessments to identify and quantify potential patient harms from Generative AI adoption. Engage multidisciplinary teams, such as clinicians, IT security, legal, compliance, risk management, and GenAI engineers, to address risks such as clinical inaccuracies, operational failures, and privacy breaches.
• Break Data Monopolies with HIEs: Utilize Health Information Exchanges (HIEs) to improve data availability and address data monopoly risks. These platforms can aggregate and integrate data from various providers, helping to democratize access to high-quality data. HIEs can also offer GenAI as a shared service, enabling smaller providers to leverage insights from large datasets. Thus, they can promote a more equitable healthcare system where technology benefits all providers and enhances care quality for underserved communities.

Major Future Trends of Generative AI in Healthcare

As technology evolves, many exciting Generative AI trends are emerging in the healthcare sector.

• Accurate Diagnoses and Precision Medicine

Generative AI is expected to further enhance diagnostics and precision medicine. By generating highly detailed medical images, it will be able to detect and analyze diseases with unprecedented precision, leading to more targeted and personalized treatments.

• Collaborative AI and Human Interaction

In the future, we will see a closer partnership between GenAI systems and healthcare professionals. This effective collaboration between human expertise and GenAI capabilities will significantly improve patient care and support better decision-making.

• Integration with Big Data and EHRs

The integration of Generative AI with big data and EHRs is brimming with promises. By accessing extensive patient data, GenAI models can identify patterns, generate valuable insights, recognize trends, predict outcomes, and refine treatment plans, thereby improving the overall efficiency and efficacy of healthcare services.

• Multi-Modal Generative AI

Looking ahead, Generative AI will increasingly adopt multi-modal approaches, incorporating diverse data types such as genetic information, clinical notes, imaging, and sensor data. This comprehensive approach is expected to boost our understanding of patient health and enhance diagnostic and treatment processes.

• Continuous Learning and Adaptive Systems

To remain effective in the dynamic healthcare environment, Generative AI systems must continuously learn and adapt. Future GenAI models will incorporate continuous learning techniques, allowing them to update their knowledge base, respond to new data and emerging diseases, and deliver increasingly accurate results over time.

Generative AI has incredible potential to improve healthcare systems by refining diagnostic accuracy, speeding up drug discovery, streamlining operations, improving medical training, and more. As Generative AI technologies continue to develop, their integration into health care will become even more widespread and impactful.

However, successfully adopting Generative AI in healthcare requires balancing its transformative potential with inherent risks. The rapid advancement and intricacy of this technology make it even more challenging for medical centers to implement on their own.

That’s where a reliable partner like Neurond comes in. Neurond specializes in providing tailored Generative AI consulting services for healthcare, focusing on various areas from drug discovery and medical imaging to personalized medicine. Rest assured our expert team will work closely with your organization to integrate GenAI seamlessly into your workflows for enhanced efficiency and patient care.