The Ai Revolution:

Redefining Healthcare Marketing

Harnessing the potential of Generative Ai

The WHO reports an estimated shortage of 10 million health workers in 2030 - particularly in low and lower-middle-income countries. The projected labour shortage is anticipated to further strain an already burdened industry. Healthcare and pharmaceutical organisations are hoping that solutions driven by artificial intelligence (AI) will improve the efficiency of industry professionals.

Generative artificial intelligence (AI) has already demonstrated its utility in two broad areas:

Generating operating efficiencies

Gen AI hastens the research process by identifying drug targets quickly, driving sales and optimising supply chains. Deloitte's findings reveal that almost 70% of biopharmaceutical executives consider the adoption of gen AI essential for research and discovery.

Assisting with patient-facing tasks

Automating administrative tasks, generating patient data summaries, enhancing diagnoses, and suggesting treatment options. By reducing time spent on repetitive tasks, clinicians will have more resources to focus on patient care and improving the user journey of patients. For example, medical chatbots can be utilised to deliver personalised information to patients.

Catching the Ai Wave

EU Legislative Support

The recently drafted AI Act in December 2023 by the European Union ensures that AI systems used within Europe adhere to transparency requirements, ethical standards (such as disclosing any content generated by AI), are traceable, safe, environmentally friendly and non-discriminatory.

Big Tech involvement

Tech companies have an open interest in applying gen AI technology in the healthcare industry. Most recently, an example is Mayo Clinic's partnership with Microsoft, where the Microsoft 365 Co-pilot is being tested with clinical staff, doctors and healthcare workers, hoping to reduce the time spent on monotonous tasks to improve patient care. Google Cloud and Accenture formed a partnership to explore possible solutions using Google's large language models (LLMs). These include improving operational efficiency, patient engagement, caregiver support, research and development.

Government Support

Determined not to be left behind in the AI revolution, governments all around the world are heavily investing in, increasing regulation and implementing the use of generative AI for their healthcare systems with varying speeds and approaches. The WHO reports that up to 70 countries have agreed to step up investments in primary health care by 2030 to improve access to healthcare and make services more affordable.

Positive Ground-Up Sentiment

In a 2023 survey by Wolters Kluwer about generative AI in Healthcare, though one in four American consumers (27%) reported feeling nervous about the technology, almost half (44%) were ready for gen AI to be used in healthcare.

Specifically, respondents were open to gen AI being used in less invasive processes such as annual screenings/exams (54%), cancer screening (45%) and diagnosis of diseases (43%). Respondents were less accepting of gen AI being used in invasive procedures such as surgery (25%) and mental health support (31%).

Readiness for the use of Gen Ai in healthcare

Percentage of consumers per use case:


Part of an annual screening/exam


Cancer screening


Diagnosis of diseases


Pain management


Decisions on treatment


Mental health support



Consumers believe that Gen Ai could improve healthcare access

According to a survey by Deloitte, over half (53%) of respondents believe gen AI could help reduce wait times in healthcare. Additionally, almost two in three (63%) respondents who have used gen AI for health reasons believe it could potentially make healthcare more affordable and remove barriers to accessing critical healthcare.

Barriers to accessing healthcare:


Even though respondents (41%) were interested in educating themselves about their health (Edelman), almost 50% of consumers indicated cost as the biggest barrier to disease prevention (PwC). One in five patients (19%) have reported deferring care for their medical issue (McKinsey) because they believe that the Healthcare system is biased against their income.

Lack of information

Lack of information is a significant barrier to healthcare access for 48% of consumers (Edelman), and nearly a quarter, 23% (PwC), do not invest in disease prevention simply because they do not understand how prevention works. A total of 50% of respondents (Edelman) reported that the lack of information, changing health recommendations and contradictory expert advice were significant barriers to better health.

What does this mean for Marketers?


Applications across healthcare marketing include tailored product recommendations, creating descriptions, headlines and images to enhance campaigns.

Experimenting with AI for solutions to break down barriers preventing consumers from accessing critical healthcare. For example, the World Economic Forum suggests LLMs trained on high-quality data in different languages could help consumers access care, regardless of their location or cultural context.

Explore how gen AI can enhance the user experience of patients. For example, Amazon Pharmacy uses gen AI to provide customers with up-to-date medication prices and speedy answers to their queries.


Scenario planning is useful for companies to understand and anticipate potential threats to healthcare caused by gen AI. For example, designing plans for possible scenarios - such as intentional sharing of disinformation. By putting in place safeguards, marketers will be better equipped to protect their brands when and if such a situation arises. Companies should start developing an internal governance structure for the use of generative AI, outlining guidelines in accordance to the organisations' goals.


Empower your audiences. With the rise of inflation, consumers are worried about rising costs of healthcare. Providing education through marketing efforts help consumers make informed decisions about self-care, take control of their health and allow brands to build trust and credibility.

Foster partnerships with industry players and stay abreast of news about small pilot-scale Ai projects that will provide valuable insight about risks and gains in different use cases.

Considerations for the implementation of Ai

The Human element

Human supervision is required to ensure AI output is reliable and accurate. Rather than relying on generative AI for content creation, marketers should view generalised AI as a collaborative tool to assist in brainstorming and idea generation processes. AI models still make mistakes and do not have emotions to deal with sensitive topics in an empathetic manner. Using generative AI also presents a risk of generating content that contains inappropriate responses, misrepresentation and misinterpretation caused by a lack of contextual understanding. Human oversight and involvement in the use of generative AI is imperative. In light of ethical concerns, the human using the tool has to be responsible for AI outputs that are used or implemented.

Evolving restrictions on use of gen AI in healthcare

In 2023, the EU reached a provisional agreement on the AI Act. Brands who do not comply with these guidelines are likely to face disciplinary action. Even though governments around the globe are pursuing best practices for AI, regulations will vary. It is crucial for brand managers to stay updated on specific laws in their country of operation.
To maintain brand trust, organisations should be transparent about the source of AI-generated content.

Waning consumer trust caused by cybersecurity breaches and data leaks

According to the United Kingdom's Information Commissioner's Office, cyber-related attacks in the health sector more than tripled from 3% in 2018, to 10% in 2022. The Health Service Executive (HSE) recorded more than 500 data protection breaches in 2022. In mid 2023, it was reported that 8 in 10 health organisations in the UK have had a security breach since 2021. In late January 2024, a data breach in the health insurance sector compromised the personal information of over 33 million people in France.

The WHO has raised concerns about the rapid adoption of LLMs within healthcare, noting the lack of caution taken when using these platforms could hurt patients. Motivated by financial gain, cybercriminals collect sensitive data to extort or sell. It is natural for consumers to lose trust in brands who have experienced data leaks and consciously choose to engage with organisations that seem more secure.

To build resilience, a sizeable budget must be allocated to ensure data security, which is fundamental to patient trust and system integrity. Should there be an instance of a data breach, marketers should respond swiftly to mitigate further damage to the brand's reputation. Brands must take accountability for its oversight and engage in transparent communication directly with customers.

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