Digital Healthcare & the role of AI: The Innovation race

Appinio Research · 02.08.2023 · 14min read

Digital Services and AI in the healthcare industry

Artificial Intelligence (AI) has been everything, everywhere and all at once these days.


AI has emerged as a transformative technology with far-reaching implications across many industries, from autonomous vehicles to virtual assistants, AI is changing and will profoundly revolutionise the way we live and work. 

The day of the official launch of Chat GPT (November, 30th 2022) has made history, 100 billion users in two months from release date.


So what other industries are going to be disrupted next?


In the latest volume of the Appinio Hype Train Report we are going to have a closer look at how AI will change the healthcare industry. The potential benefits of AI in healthcare are extremely vast, ranging from improved patient outcomes to increased efficiency and cost savings.

By leveraging the power of AI, healthcare professionals can make more informed decisions, provide accurate diagnoses, and offer personalised treatment plans. 


Therefore, we asked our Appinio panel how they currently interact with the healthcare industry and whether they’d be willing to see more AI introduced in the healthcare sector.


Download the report


What is AI?

Our Appinio Hype Train Report on digital healthcare also investigated Brits' level of knowledge of AI:

  • 30% said they knew what AI was and they could explain it to others
  • 51% said to have a basic understanding
  • 15% said they knew little about it
  • 4% said they didn’t know what AI was


But let’s start from the beginning.


AI, or Artificial Intelligence, refers to the simulation of human intelligence in machines that are programmed to process information and learn like humans. 


It is a branch of computer science that focuses on creating intelligent systems capable of performing tasks that typically require human intelligence, such as problem-solving, decision-making, language understanding, pattern recognition and whether it’s appropriate to tell dad’s jokes at work (it never is).


AI encompasses a broad range of techniques and approaches, including machine learning, natural language processing, computer vision, expert systems, and more. Machine learning, in particular, plays a significant role in AI, enabling systems to learn from data and improve their performance over time without explicit programming.


The goal of AI is to develop systems that can perform tasks autonomously, adapt to new situations, and exhibit behaviors that are typically associated with human intelligence. 

These systems can be designed for specific applications, such as virtual assistants, image recognition, autonomous vehicles, and recommendation systems, or they can be developed to possess general intelligence, which would involve understanding and learning across a wide range of tasks and domains.


It's important to keep in mind that AI is an evolving field, and even if incredible progress has been made, achieving human-level intelligence or creating truly self-aware machines remains an ongoing area of research and development.

AI in Healthcare: It’s Going to Happen

Digitalisation and the introduction of AI softwares in healthcare hold the promise of transforming the industry radically, and this is true for many other industries


The healthcare industry is considered one of the marvels of the world.

The sector has improved rapidly, providing an incredible level of basic care for many and therefore raising life expectancy around the world: we live longer than some centuries, but even longer than just a few years ago!


But as longevity increases and many countries are already facing an increasing aging population, healthcare systems face growing demand for their services, rising costs and a workforce that is struggling to meet the needs of its patients.


And in recent years, COVID-19 pandemic has exacerbated the situation: the health systems all over the world entered in emergency mode, students and residents were thrown into the battlefield as the demand for health professionals became crushing and many who were about to retire decided to hold on and help. On the other hand, patients in need of simple or routine procedures experienced delays and more.

The pandemic seems now under control, but the healthcare system was not ready to respond and some obstacles persist to this day.

Many professionals abandoned the profession because they burned out, others realised that wasn’t their path, many medicine students renounced their dream of becoming a doctor and now the UK is experiencing a shortage of physicians and 7.4 million patients on waiting lists.


The pandemic served as a propeller to uncover the underlying conditions of a system that needs profound change, and if pandemics will become more frequent, as experts warn, we are going to get in deep waters.


So how will AI support the healthcare system?

AI Applications in Healthcare

AI is already being used in healthcare, but currently a lot of work is done behind the scenes, so patients (or consumers) don’t experience AI firsthand.


The incredible progress already made in the healthcare sector not only means better life expectancy, but a massive amount of data produced every day. 

Documentation is created every day from monitoring diseases, clinical trials, new discoveries, new experimental treatments, and so much more. 


It’s clear that making sense of all this data in a timely manner becomes a challenging task…


Leveraging AI enables healthcare providers to analyse vast amounts of medical data quickly and accurately, leading to enhanced diagnostic capabilities. AI-powered algorithms can assist in early detection of diseases, aid in drug discovery and development, and optimise treatment plans for individual patients.


Let’s take a closer look.


Diagnostic Assistance

AI has shown great potential in assisting healthcare professionals with diagnostic tasks. 


Machine learning algorithms can analyse medical images, such as X-rays, CT scans, and pathology slides, to identify patterns and abnormalities with high accuracy. 

This technology helps radiologists and pathologists make more precise and timely diagnoses, improving patient outcomes.


Moreover, AI facilitates faster and more accurate diagnoses. 

Thanks to machine learning algorithms, AI systems can learn from vast datasets and identify patterns that may not be readily apparent to human observers. 

This allows early detection of diseases and the formulation of proactive treatment strategies, potentially saving lives and reducing the burden on healthcare systems.


Drug Discovery and Development

The process of discovering and developing new drugs is time-consuming and costly. 


AI is helping accelerate this process by analysing vast amounts of biological data, including genetic information, molecular structures, and scientific literature. AI algorithms can identify potential drug targets, predict drug efficacy, and even design new molecules, making the drug discovery process more efficient and cost-effective.


Personalised Medicine

AI is paving the way for personalised medicine, where treatment plans are tailored to an individual's unique characteristics. 

By analysing patient data, such as genetic information, medical history, and lifestyle factors, AI algorithms can generate personalised treatment recommendations. 


Additionally, AI has the potential to enhance patient outcomes. By leveraging patient data, including electronic health records and wearable devices, AI algorithms can generate personalised treatment plans tailored to individual patients. 

This level of personalisation ensures that healthcare interventions are optimised for each patient's unique circumstances, leading to more effective and efficient care.


Patient Monitoring and Care

AI-powered tech can continuously monitor patients, collect vital signs, and analyse real-time data to detect any anomalies or changes in health conditions. 

This is great for early intervention and timely medical attention, particularly for patients with chronic diseases. Additionally, AI chatbots and virtual assistants can provide personalised support, answer patient queries, and offer basic medical guidance.


Administrative Tasks and Workflow Optimisation

AI can streamline administrative tasks, reducing the burden of paperwork and optimising workflows within healthcare organisations. 


Natural Language Processing (NLP) enables automated extraction and analysis of information from medical records, facilitating faster and more accurate coding, billing, and documentation. AI-powered systems can also automate appointment scheduling, resource allocation, and inventory management, improving overall operational efficiency.


This integration of AI in healthcare has the potential to bring down costs by a lot, allowing resources to be allocated more effectively.

These are only some of the areas that are going to be affected by the introduction of AI technologies and it is already clear that AI has the potential to transform the healthcare industry.

Consumer Perception of AI in Healthcare

As AI continues to be increasingly implemented in the healthcare industry, understanding consumer perceptions and attitudes towards this technology is crucial. 

Investigating consumer opinions provides insights into how AI is perceived and accepted in the context of healthcare. 


So let’s deep dive into the Appinio Hype Train Report results!


General Awareness and Understanding of AI

Who hasn’t heard about AI in this day and age?
We could safely assume a lot of people have, but their level of understanding may vary. 


Some may have a solid grasp of AI concepts and its potential applications, while others may hold misconceptions or have limited knowledge. 


“The robots are coming for us!” Does it ring a bell?


Understanding the general awareness and understanding of AI among consumers is understanding how consumers will embrace AI-driven healthcare solutions.


Our Appinio Hype Train Report on digital healthcare tells us that

  • 30% of Brits know what AI is and they could explain it to others
  • 51% said to have a basic understanding
  • 15% said they know little about it
  • 4% said they don’t know what AI is.


With so many consumers rating their knowledge as basic it’s no wonder many have concern about using AI in the medical field.

Over one third (34%) say they have (full) confidence in the use of artificial intelligence in the medical field, as well as 28% leaning towards an optimistic view of AI. 

Millennials and 35-44-year-olds are the most optimistic, while older generations are the most concerned by the introduction of this new technology.


Levels of Trust in AI

The Appinio Hype Train Report has also investigated consumers’ trust in AI, and even if level of trust in AI producing diagnosis are lower (64%) than the ones for a traditional doctor (69%), the gap is minimal and we could safely assume that it will be bridged the more consumers get familiar with the technology and its applications.


Americans' levels of trust in AI, US respondents seem to trust more a diagnosis made by a doctor supported by an AI, rather than a diagnosis made by an AI supported by a doctor.


Nevertheless, some AI use cases seem more trustworthy than others.


AI earns the highest levels of consumers’ trust in the following applications:


  1. Evaluation of X-ray images/MRI/CT (33%)
  2. Health tracking and health prognosis (32%)
  3. Treatment plans (28%)


It’s no surprising to see higher levels of trust for these applications, rather than for an AI produced diagnosis (26%).


  1. Clear Visualisation of Results
    Applications like evaluating X-ray images, MRI, and CT scans often produce visual outputs that consumers can easily understand. When AI assists in interpreting these images, consumers can visually see the results, making it easier for them to trust the technology's accuracy.

  2. Narrower Scope and Specificity
    Applications that focus on treatment plans or health tracking tend to have a narrower scope compared to making complex medical diagnoses. The AI's role in suggesting treatment plans or monitoring health parameters may be seen as less risky, leading to higher consumer trust.

  3. Quantitative Data Interpretation
    AI excels at processing and interpreting large volumes of quantitative data, such as monitoring vital signs or analysing patterns in health data. Consumers may be more comfortable trusting AI with tasks that involve data analysis rather than subjective decision-making.

  4. Objective Nature of Information
    AI algorithms can provide objective information based on data analysis, making consumers more likely to trust the results. In contrast, making a definitive diagnosis may require considering a wide range of subjective factors, which consumers may feel more comfortable leaving to human experts.

  5. Supportive Role
    But more importantly, in health tracking and health prognosis, AI is often used to complement medical advice and not solely relied upon for definitive diagnoses. Consumers may trust these applications more because they perceive the technology as a support tool rather than a replacement for human expertise.

Challenges and Key Concerns of AI in Healthcare

While AI offers immense potential in healthcare, it also presents challenges and limitations that need to be addressed. 


  • Data Privacy and Security

    The use of AI in healthcare relies heavily on access to vast amounts of sensitive patient data.
    Ensuring data privacy and security is essential to maintain patient trust and comply with regulations such as HIPAA (Health Insurance Portability and Accountability Act). Healthcare organisations need to ramp up their efforts in implementing robust security measures to protect patient data from unauthorised access, breaches, and misuse.


  • Bias and Algorithmic Fairness

    AI algorithms are trained on vast datasets, but they can still produce biased results. Unfortunately, it’s a very well known issue in the field and experts are still trying to understand how to tackle it.

    If not properly addressed, these biases can result in disparities in healthcare delivery and outcomes across different demographic groups.


  • Regulatory and Legal Considerations

    The integration of AI in healthcare raises important regulatory and legal considerations. Regulatory frameworks must be updated to address the unique challenges posed by AI, including issues of accountability, liability, and transparency. If an AI algorithm produces a wrong diagnosis, who’s going to be accountable? The programmer, the software company?

    Ensuring compliance with existing regulations while adapting to the evolving AI landscape is essential to foster responsible and ethical use of AI in healthcare.


Addressing these challenges and limitations is crucial to foster the responsible and effective implementation of AI in healthcare.


The Future of AI in Healthcare

AI in healthcare is continuously evolving, and several advancements and trends are shaping its future. Some potential advancements include:

  • the integration of AI with wearable devices for real-time health monitoring, 
  • the use of natural language processing for improved clinical documentation, and 
  • the development of AI-powered robotic systems for surgical assistance


Additionally, the utilisation of big data and machine learning algorithms to analyse population health trends can lead to more effective public health interventions. 


But AI has the potential to address many other healthcare challenges.

  • It can help overcome the shortage of healthcare professionals by augmenting their capabilities, assisting in repetitive tasks or taking charge of preliminary patient assessments. 
  • AI can also facilitate early disease detection and intervention, leading to improved health outcomes and reduced healthcare costs. 

Furthermore, AI-powered predictive analytics can help healthcare organisations anticipate patient needs, optimise resource allocation, and improve operational efficiency.


AI is transforming healthcare by improving accuracy, efficiency, and patient outcomes. 

However, addressing consumer concerns and challenges like data privacy, regulations, and bias is crucial for widespread acceptance. 


Future advancements in wearable devices, natural language processing, and population health analytics offer exciting opportunities. AI can help overcome healthcare challenges, enhance disease detection, and optimise resource allocation. 


Continued research, development, and collaboration are essential for realising the full potential of AI in healthcare and improving accessibility, quality, and affordability.


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