Generative AI in Healthcare: 9 Use Cases & Upcoming Solutions

Introduction

Artificial intelligence is rapidly advancing, especially in healthcare. We are constantly provided with new impressive examples of how AI can positively change our lives by opening doors to profitable businesses, innovations, and investments. What are the AI use cases in healthcare and top trending solutions in generative AI in the industry?

What is Generative AI?

Generative AI is a type of Artificial Intelligence that’s all about creating data or content. Its work is based on deep-learning neural networks that learn from the provided examples and data inputs. When given lots of information, generative AI can understand patterns and data structures, and generate new content similar to the one analyzed. Just like AI in general, this technology is dynamically improving and is predicted to become a game-changer.

Generative AI can be used to create various types of data:

• Text
• Images
• Videos
• Music
• Voice
• Conversations
• Design elements
• Simulations
• Realistic scenarios.

The outputs listed above have versatile applications across various business functions like customer interactions, marketing, sales, or education. Moreover, these capabilities can be applied in a number of industries, with healthcare being a notable example. How exactly can generative AI be used in this sector?

AI use cases in healthcare

AI is currently opening new opportunities for innovation and investments. In healthcare, the AI market is expected to grow about 37,5% by 2030 at a CAGR. Generative AI can be used in many ways within the industry, e.g. to automate processes, improve customer experience, enhance learning and experimenting, boost productivity, and accelerate medical diagnoses. 

Let’s take a close look at the 9 examples of using generative AI in healthcare.

Medical Imaging Analysis

STATUS: VALIDATED

How generative AI can help

Generative AI can be used to obtain high-resolution, detailed medical images. This can be applied specifically to:

• diagnosing - AI-powered pictures are capable of highlighting the details of pathologies, e.g. in radiology, which leads to more accurate diagnoses;
• predicting the disease progression - AI can analyze even tiny changes in the delivered medical images and predict the pace and direction of disease development. This can significantly contribute to improving the treatment process. 

How this solution is used already

Zebra Medical Vision is a company that uses AI to read medical imaging and provide insights. Their algorithms analyze data to detect diseases and provide a more accurate diagnosis.

Google's DeepMind has developed algorithms that can spot eye diseases in scans. The results showed that DeepMind's system could make correct referrals in over 94% of cases, matching or beating expert clinicians.

Benefits of using AI in Medical Imaging Analysis

• Enhanced diagnostic accuracy due to high-resolution images.
• Early detection of diseases, leading to better patient outcomes.
• Improved treatment planning based on detailed imaging.

Challenges of using AI in Medical Imaging Analysis

• Need for vast amounts of data to train the AI models.
• Risk of over-reliance on AI, potentially overlooking human expertise.
• Ethical concerns regarding patient data privacy.

Innovation inspiration - how to use this advancement?

• Development of platforms that integrate with medical imaging devices to provide real-time analysis. 
• Creating cloud-based solutions where medical professionals can upload images for AI analysis, aiding in remote diagnostics.
• Development of platforms that allow radiologists worldwide to upload and compare scans, enhancing global knowledge and improving diagnosis accuracy.

Developing Treatment Plans

STATUS: VALIDATED

How generative AI can help

Personalized treatment planning is the next opportunity created by generative AI. Technology contributes to this field by analyzing vast quantities of data related to patient’s test results, medical history, and genetics. Based on such information, AI can:

• deliver recommendations for the right intervention tailored to the patient’s condition and characteristics;
• develop personalized treatment strategies;
• forecast the likelihood of complications after treatment and assess risks;
• optimize the dosage of drugs as well as schedule treatments for individuals, based on data related to their drug response to maximize the efficiency of curation;
• plan surgeries thanks to 3D modeling, virtual simulations, and highlighting the anatomical structures of the patient. This allows for performing surgeries with high precision.

How this solution is used already

IBM Watson Health uses AI to analyze the meaning and context of structured and unstructured data in clinical notes and reports. It aids oncologists in providing personalized treatment options based on the patient's medical information and a vast database of clinical evidence.

Benefits of using AI to develop treatment plans

• Personalized treatment strategies tailored to individual patients.
• Improved patient outcomes due to optimized drug dosages and treatment schedules.
• Reduced risk of complications post-treatment.

Challenges of using AI for treatment plans development

• Ensuring the AI recommendations align with medical best practices.
• Potential biases in AI recommendations based on training data.
• Dependence on accurate and comprehensive patient data.

Innovation inspiration - how to use this advancement?

• Development of personalized health platforms that take in patient data and provide tailored treatment plans. This can also be integrated into existing hospital management systems.
• Creation of patient portals where individuals can input their symptoms and receive potential treatment plans or recommendations before visiting a doctor.

Medical Reports Generation

STATUS: VALIDATED

How generative AI can help

The next area where generative AI can be applied is supporting administrative tasks in healthcare. AI can help to:

• precisely analyze patient data, including all types of content: notes, images, and numbers;
• generate structured and consistent medical reports including consultation outcomes, test results, and medical interventions performed on the patient;
• create electronic records and comprehensive documentation;
• reduce medical staff’s workload and effort, and increase the overall efficiency of the healthcare system.

How this solution is used already

Google Cloud's Healthcare API includes tools for converting various medical data types into a format that can be analyzed by more traditional machine learning tools.

Google's EHR (Electronic Health Records) uses AI to predict patient stays, readmissions, and other important metrics.

Benefits of using AI to medical reports generation

• Streamlined administrative processes leading to increased efficiency.
• Consistent and structured medical reports.
• Reduced workload for medical staff.

Challenges of using AI for medical reports generation

• Ensuring the accuracy of AI-generated reports.
• Risk of data breaches and privacy concerns.
• Dependence on the quality of input data.

Innovation inspiration - how to use this advancement?

• Development of voice-assisted note-taking during patient visits, converting speech to structured EHR entries.
• Development of platforms that auto-generate medical reports based on patient data. These platforms can also integrate voice recognition to transcribe doctor's notes.

Research and Drugs Development

STATUS: VALIDATED

How generative AI can help

Generative AI is capable of accelerating medical research, including drug discovery. How? AI algorithms can:

• analyze large datasets of chemical structures and their properties;
• detect and apply learned patterns by combining molecules into substances;
• develop fresh theories based on outcomes typically undetectable by the human eye;
• shorten the traditional, long, tedious, and expensive drug discovery procedure thanks to involving virtual compounds that reduce expenses and time;
• predict and mitigate potential risks and side effects of developed drugs.

How this solution is used already

Atomwise uses AI for drug discovery. Their technology predicts how different chemicals would behave and how likely they would be to make an effective drug.

They've launched numerous projects in areas like cancer, infectious diseases, and fibrosis.

Benefits of using AI for drugs development

• Accelerated drug discovery process.
• Cost savings due to virtual compound testing.
• Potential for discovering novel drug compounds.

Challenges of using AI for drugs development

• Ensuring the safety and efficacy of AI-generated drug compounds.
• Ethical concerns regarding AI-driven drug testing.
• Dependence on vast and diverse chemical datasets.

Innovation inspiration - how to use this advancement?

• Development of platforms that simulate drug interactions in the human body, predicting side effects or potential drug-drug interactions.

Improving Telemedicine

STATUS: VALIDATED

How generative AI can help

Following a surge that occurred after the outbreak of the COVID-19 pandemic, AI-supported telemedicine is now stepping into a brand-new era of providing healthcare remotely. Generative AI for healthcare can now help to:

• monitor the patient’s condition via sets of devices and sensors in real-time, send alerts about health issues, and let medical staff intervene as fast as possible;
• support patients with consultations based on delivered images and descriptions of symptoms;
• develop chatbots that provide patients with basic medical advice without medical professionals' involvement. 

How this solution is used already

Babylon Health offers a chatbot that provides medical consultation based on the medical information fed into it.

Benefits of using AI to improve telemedicine

• Real-time patient monitoring leading to timely interventions.
• Increased accessibility to medical consultations.
• Efficient patient triaging and support.

Challenges of using AI to improve telemedicine

• Ensuring the accuracy of AI-driven diagnoses.
• Dependence on reliable internet connectivity.
• Ethical and privacy concerns with remote patient monitoring.

Innovation inspiration - what else can be done with this solution

• Development of virtual health assistants that can schedule appointments, provide medication reminders, and offer basic health advice.
• Development of telemedicine platforms with integrated AI tools for real-time patient monitoring and diagnostics.

Improving Diagnosing

STATUS: VALIDATED

How generative AI can help

Generative AI can be significantly useful in disease diagnosis. Thanks to its ability to analyze large datasets and medical images, generative AI can:

• recognize patterns by comparing e.g. photos of skin diseases, X-rays, MRIs, and so on, and provide diagnoses based on detected abnormalities;
• get to conclusions and diagnoses much faster than human professionals can;
• recognize even subtle changes and alert about anomalies even in an early stage, before they can be detected by the human eye; 
• predict the further disease development and progression which helps to develop the right treatment plan.

How this solution is used already

PathAI is developing machine learning technology to assist pathologists in making more accurate diagnoses.

Zebra Medical Vision offers algorithms that read medical imaging to assist radiologists in diagnosing diseases.

Benefits of using AI to improve diagnosing

• Early and accurate disease detection.
• Comprehensive analysis of medical images.
• Improved patient outcomes due to timely interventions.

Challenges of using AI to improve diagnosing

• Risk of false positives or negatives.
• Over-reliance on AI at the expense of human expertise.
• Ethical concerns regarding AI-driven diagnoses.

Innovation inspiration - what else can be done with this solution

• Creation of mobile apps that can analyze user-uploaded images (like skin lesions) to determine if they need further medical attention.
• Creation of diagnostic tools that can be integrated into existing medical platforms or used as standalone apps.

Mental Health Assistance

STATUS: EARLY STAGE

How generative AI can help

The next creative and meaningful use of generative AI in healthcare is supporting mental health. AI-powered chatbots can:

• provide patients with mental health assistance and counseling that deliver emotional support and coping strategies;
• provide instant, ad-hoc therapeutic dialogues simulating human-like conversations;
• study personal characteristics and behavior trends to discover how people respond to treatments;
• create personalized mental health support plans that improve the effectiveness of treatments and enhance the individuals’ well-being.

How this solution is used already

Woebot Labs developed an AI chatbot therapist that uses principles of cognitive-behavioral therapy to assist users. The chatbot is designed to help users with anxiety and depression.

Benefits of using AI for mental health assistance

• Instant therapeutic support for patients.
• Personalized mental health support plans.
• Reduced burden on mental health professionals.

Challenges of using AI for mental health assistance

• Ensuring the efficacy and safety of AI-driven therapies.
• Ethical concerns regarding AI-driven mental health interventions.
• Risk of misdiagnosis or inappropriate advice.

Innovation inspiration - what else can be done with this solution

• Development of mental health platforms that use AI to provide counseling, track patient progress, and offer personalized treatment plans.
• Creating platforms that connect users with real therapists based on AI-driven symptom analysis, ensuring a good patient-therapist match.

Medical Robots

STATUS: EARLY STAGE

How generative AI can help

The next promising idea to use AI in healthcare is developing medical robots that support surgeries or other medical procedures. These robots might be:

• trained by providing them with a vast amount of data and medical images presenting human anatomy;
• able to perform their work similarly (or even more efficiently) to human medical professionals;
• capable of interpreting health conditions and acting according to medical best practices;
• increase the treatment efficiency;
• reduce the workload of medical staff and make medical services more available and affordable.

How this solution is used already

Intuitive Surgical's da Vinci robot uses AI to assist surgeons during operations, providing them with enhanced stability and precision.

Benefits of medical robots

• Precision in surgeries and medical procedures.
• Increased efficiency in treatment delivery.
• Potential for remote surgeries.

Challenges connected to using medical robots

• High costs of development and implementation.
• Ethical concerns regarding robot-driven medical procedures.
• Dependence on reliable technology infrastructure.

Innovation inspiration - what else can be done with this solution

• Development of training platforms where surgeons can practice procedures in a virtual environment powered by AI.
• Creating software that controls and guides medical robots. 

Medical Simulations

STATUS: EARLY STAGE

How generative AI can help

Generative AI can be also used for training purposes. Based on delivered data inputs, the technology can:

• develop scenarios used by doctors in the training process;
• create medical simulations that ensure hands-on training, tangible experience, and high precision of actions;
• leverage the level of knowledge and practical skills among medical staff; 
• allow for experimenting and trying out new approaches and solutions on real cases but in a virtual where the patient can’t get harmed. Thanks to this, medical staff can practice applying procedures, improve decision-making, and enhance cooperation between healthcare professionals.

How this solution is used already

Osso VR offers virtual reality training for surgeons, allowing them to practice procedures in a risk-free environment.

Benefits of using medical simulations

• Hands-on training for medical professionals.
• Safe environment for experimenting with new procedures.
• Enhanced skill development and knowledge retention.

Challenges of using medical simulations

• Ensuring the realism and accuracy of simulations.
• High costs of developing and maintaining simulation technologies.
• Risk of over-reliance on simulations at the expense of real-world experience.

Innovation inspiration - what else can be done with this solution

• Development of virtual reality or augmented reality platforms for medical training. That kind of platform could simulate surgeries, patient interactions, and other medical procedures.
• Creating AI-driven patient avatars that can respond in real-time to medical procedures during training simulations.

Challenges in Generative AI for Healthcare

While leveraging generative AI in healthcare offers evident advantages, there are also several challenges that require careful consideration before embarking on generative AI healthcare projects. The major drawbacks and risks are:

• Image Authenticity: Difficulty in distinguishing between AI-generated and genuine medical images generates a risk of healthcare data being influenced or manipulated by AI.
• Diagnosis Reliability: Diagnoses shared through online channels may lead to potential overreliance and neglect of physical medical treatments, appointments, and examinations, which generates a huge risk of health and safety issues in case diagnoses are inaccurate.
• Limited Datasets: Certain healthcare domains may face difficulties in gathering sufficiently large datasets for precise conclusions.
• Incomplete Data Representation: Challenges arise when input data inadequately represents certain patient groups, which may lead to inaccurate conclusions.
• Over-Reliance on AI: Doctors might excessively depend on AI-generated results, put trust in biased results, and sideline their own expertise.
• Data Security: The sensitivity of medical data raises concerns about inadequate security measures.

Generative AI in healthcare - where to start?

Is investing in AI for healthcare worthwhile? Absolutely. Generative AI holds the potential to not only advance modern medicine but also enhance the quality of life for everyone. This field offers groundbreaking product concepts that can drive your company's growth and help you develop products that will serve humanity. 

So, how to manage potential risks and successfully kick off with a generative AI healthcare project? First, start with building a strategy. Before you invest in developing a generative AI product, discuss it with technology experts who will help you to develop the best business model that will let you leverage the opportunities of the new technology. The right recommendations on data management, analysis tools, systems architecture, and proper training can help you mitigate risks and explore AI for healthcare to the fullest.

If you currently seek support, don’t hesitate to contact us. With experience in developing healthcare products, at Brainhub we are aware of the challenges arising in the industry. We will help you to validate your idea and execute it in the most efficient way.