What is Generative AI in Healthcare Industry and Its Benefits, Challenges, and Potential?

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Generative AI is deep-learning models that create brand new outputs when prompted through models or algorithms using the vast data they are trained on. The outcomes include text, videos, photos, code, or 3D renderings. Generative AI creates new work similar to the original source by encoding a simplified representation of their training data.

With the concept of Generative AI, we embark on a journey that goes beyond the realm of binary logic. Now, AI is not just an executor of tasks but also an inventor. It has become a creative companion that creates original outputs that inspire and astonish us. Generative AI has opened gates to an entirely new world of innovation where machines reflect some level of human-like creativity.

During the late 1950s, when machine learning was introduced, scientists explored the concept of utilizing algorithms to create new data. One of the early instances of Generative AI was the use of a statistical model called Markov Chain to generate new data sequences through inputs. Further, in 2013, deep-generative models, VAEs, Variational autoencoders were used to create realistic images and speech.

The healthcare industry embraces Generative AI with great vigor as it has immense potential to revolutionize several aspects, including medicine discovery, and more. It can analyze massive amounts of data to identify patterns and share significant insights about users to healthcare providers.

According to Precedence Research, the global generative AI in the healthcare industry market is valued at USD 1.07 billion, and it is poised to witness a staggering growth to reach around USD 21.74 billion by 2032 with a CAGR of 35.14%. The remarkable market value indicates the plethora of advantages generative AI holds for the healthcare sector. Let us deep dive to comprehend how generative AI is transforming the entire contour of the healthcare industry.

Generative AI Healthcare Market Revenue


Generative AI: Easing Out the Burden of the Healthcare Industry

The deep learning neural networks in Generative AI are modeled as per the human brain structure. Functioning as multiple layers of connected nodes, it processes the information fed into the network. After each node processes the information, it is transferred to the subsequent layers, and the final layer generates the output. According to Tech Target Health Analytics, around 47.5% of health systems have adopted generative AI solutions to address their challenges.

Some of the top benefits Generative AI offers the healthcare industry include the following:

1.   Accelerating Medicine Discovery

Accelerating medicine discovery

According to Research conducted by Vox, during the early 1990s and the late 2000s, the research fraternity had to invest nearly 12 years or more to discover an effective medicine. In recent years, the advent of generative AI has eased processes in the healthcare industry. For instance, a team of researchers from MIT used AI to test Halicin and enzyme inhibitor’s efficacy against 100 million chemical compounds over the course of only a few days.

Reports suggest that 30% of the new medications will be discovered by using generative AI by 2025. Generative AI can generate new molecules that are similar to existing medicinal components by learning from a vast dataset of chemical structures. The medicinal capabilities these new molecules hold can be tested in the labs. Several processes can be fast-tracked as its deep learning algorithms can quickly identify potential therapeutics from a vast dataset of compounds. Using generative AI, researchers can create virtual combinations, test them Insilco, and bring down the time and cost involved in discovering new pharmaceutics. Generative AI also assists in designing new therapeutics as its algorithms grasp from the database of molecular structures and create new molecules that can be optimized for a specific target.

2.   Precise Disease Diagnosis

Precise disease diagnosis

Generative AI can ease processes for healthcare professionals by helping them diagnose diseases. It can learn from the dataset of medical images and analyze CT and MRI scans to identify the specific patterns associated with an illness or condition and quickly diagnose the medical condition affecting the user. For instance, Generative AI algorithms can identify a particular pattern prevalent in skin cancers by analyzing a dataset of skin images and providing accurate diagnoses to the oncologist.

3.   Developing Medical Chatbots

Developing Medical Chatbots

The healthcare industry can leverage Generative AI to create medical Chabots, paving the way for personalized medical advice and recommendations for users. In this regard, Babylon Health, a digital-first health service provider, has combined an artificial intelligence-powered platform with virtual clinical operations for care seekers to develop a Chatbot. Using generative AI, Babylon’s Chatbot provides personalized medical advice to its users by enquiring about their symptoms.

4.   Creating Personalized Treatment Plans

Creating personalized treatment plans

By analyzing a care seeker’s medical history, genetic information, and lifestyle factors, generative AI can create a treatment plan that will work best for them. For instance, by analyzing a tumor DNA and identifying the gene mutation responsible for it, generative AI’s algorithm can suggest an apt treatment plan to target the genetic mutation.

5.   Generating High-resolution Medical Imagery

Generating high-resolution medical imagery

By learning from a massive set of medical images, generative AI can enhance the quality of original images, assisting physicians and healthcare providers to detect even subtle changes in organs to arrive at precise decisions and diagnoses. Besides generating high-resolution medical imagery, generative AI can automatically detect abnormalities in the scan without any human intervention. This aspect promises faster care, which proves to be a critical touch point while healthcare providers deal with those suffering from acute trauma conditions.

6.   Expediting Research in Healthcare

Expediting research in healthcare

Generative AI algorithms can gain understanding from extensive scientific literature datasets to identify topics associated with research papers. It proves advantageous to the scientific fraternity in the healthcare industry as it enables them to identify new research areas and generate new hypotheses quickly.

7.   Hands-on-training With Medical Stimulation

Generative AI can be used to create medical simulations that will provide hands-on training to healthcare providers. For instance, University of Michigan’s generative AI program simulates a fatal sepsis situation, helping healthcare providers comprehend the severity of the condition and design effective strategies to manage and mitigate its associated risks.

8.   Facilitates Hassle-free Clinical Documentation

Facilitates hassle-free clinical documentation

Generative AI in healthcare can capture and summarize healthcare provider- care seeker consultation and ease out clinical documentation. It can consolidate the documentation by recording information, generating EHRs (Electronic health records), and simplifying complex medical jargon terminologies for user understanding.

The Downside of Generative AI in the Healthcare Industry

Though Generative AI holds great potential for the healthcare industry and is smoothening processes in this sector, it does come with certain limitations that need to be addressed.

  • Difficult to Interpret

The output created by the generative AI may not be easy to comprehend. Also, no clarification is available to understand how the algorithm arrived at a particular conclusion. It puts healthcare providers in a tough spot as they are unable to make informed decisions based on generative AI recommendations.

  • Lack of Sufficient Training

Generative AI requires large datasets for training. In several scenarios, such expansive data sets may not be available, limiting the activity and effectiveness of generative AI in the healthcare industry.

  • Absence of Transparency

As they are trained on big data, the generative AI algorithm sometimes produces biased results, leading to mistrust among care providers and users.

  • Triggers Ethical Concerns

The use of generative AI in healthcare triggers several ethical concerns associated with a breach of privacy, sharing of care seeker’s sensitive medical information, and algorithmic bias.

Future Trends of Generative AI in the Healthcare Industry

As generative AI continues to evolve and advance, it will become widely accepted and adopted in the healthcare industry, and its future trends will profoundly impact the sector.

  • Generative AI With Enhanced Diagnostics and Precision Medicine 

Soon, generative AI algorithms will become more refined, with enhanced data analysis capabilities and interpretation to accurately recognize disease or symptom patterns and suggest treatment plans.

  • Generative AI With a Broader Range of Applications 

Generative AI in healthcare settings will have a more comprehensive range of applications that includes predictive modeling of disease outbreaks.

  • Generative AI With Other Technologies

Generative AI in the healthcare industry will be integrated with wearable health devices to enhance personalized care for the users and medical imaging to create high-fidelity imagery for unprecedented accuracy.

Wrapping Up

As the technology develops, the relevance of generative AI in healthcare is becoming more evident. It enhances care, increases operational efficiency, brings down costs, and benefits healthcare providers and users seeking care. Addressing the shortcomings of generative AI, like lack of interpretability and ethical concerns, will responsibly unleash this powerful technology’s complete potential.