Introduction
AI in health care and its applications
With the advent of Artificial Intelligence, businesses are changing the way they are operating and several businesses are now successful. Especially, the field of medicine is now seeing a great impact and success with the help of AI as it allows Doctors to identify certain health conditions with great accuracy.
Artificial Intelligence is now used in multiple healthcare fields such as digital health, medical analytics, and medical edification. It is used extensively as it offers efficacy and usefulness based on the data being assimilated and created. Hence, it can extensively contribute to diagnostics, healthcare decision-making, and customized medication. It will also help in telemedicine, robotic prostheses, and eliminating the existing healthcare differences.
How AI differs from traditional forms of medicine
WHO defines traditional medicine as the “sum total of the knowledge, skills, and practices based on the theories, beliefs, and experiences indigenous to different cultures, whether explicable or not, used in the maintenance of health as well as in the prevention, diagnosis, improvement or treatment of physical and mental illness.” Traditional medicine is influenced by several cultures and their beliefs and employs treatments as per the beliefs of the specific culture. However, with AI in place, there has been a shift in medical diagnosis as complex datasets in medical research can now be identified easily and eventually help in detecting diseases more easily.
Adoption Rate of AI in Healthcare
As per NCBI, the healthcare industry in the US would save annually about $150 billion by 2026 due to the use of AI.
Global Health Goals
Improved Access to Healthcare Services
According to the World Health Organization (WHO), 60% of factors affecting one’s health and quality of life are connected to their lifestyle. These factors include the amount of exercise they do, their dietary choices, the quality of their sleep, stress-related issues, substance use, and participation in recreational activities. Now, applications that use AI and machine learning technology can give lifestyle advice and reminders throughout the day using a person’s vital signs on smartphones. These apps are expected to change how healthcare systems work, improve interactions with patients, and offer services to make patient outcomes more effective overall.
AI in EHRs (Electronic Health Records)
In 2009, the United States Department of Health and Human Services started to encourage the adoption of EHRs.
Deliberato et al. suggested that AI technology could assist healthcare providers in gathering, saving, organizing, and tracking clinical information. It can also help create personalized evaluations and plans.
AI in Diagnosis
Human errors in medical diagnostics are life-threatening. Around 5.08% of outpatient diagnostic errors are estimated to occur in the United States, affecting approximately 12 million adults each year. About half of these errors could potentially cause harm.
A radiologist, Keith Dreyer at Harvard Medical School, claimed that “Meaningful AI will improve quality, efficiency, and outcomes.”
For example, Esteva et al. trained deep convolutional neural networks (CNN) based on a dataset of 1,29,450 clinical images to diagnose skin cancer. The result showed that this system can make it extremely easy for dermatologists to identify skin cancer. They suggested that smartphones could be a cheaper way to help dermatologists to improve access to diagnostic care. Liu from Google, Inc. also reported similar results in the diagnosis of breast cancer using AI technology and Dawes et al. recently published a magnetic resonance imaging-based algorithm of cardiac motion that allowed them to predict the outcomes of patients with pulmonary hypertension accurately.
AI in Personalized Medicine
Personalised medicine is a boon of a 21st-century healthcare approach. It collects user’s data like their genetic information, emotions, environment, and lifestyle. Collecting all this information generates a lot of data, and only AI technology can combine and analyse it effectively. In this, the treatment, as well as the prevention of diseases, depends upon the individual’s conditions and hereditary.
AI in Healthcare System Management
Due to improvements in medical detection methods, over testing, overdiagnosis, and overtreatment have become common and problematic. It’s believed that about one-third of cancer cases found through screening might be cases of overdiagnosis. 30% of individuals diagnosed with asthma might not have the disease
Half of the tests done before cataract surgery were unnecessary and likely increased risks.
Therefore, medical AI technology can be used in the management of health systems for large-scale organisations. It can keep an eye on costs, treatment results, and overall well-being.
AI in Medical Robots
Robots can help in surgery. The da Vinci Surgical System is a popular robot used in surgeries, with over 3400 sets in use by 2015.
Enhance Disease Prevention and Early Detection
Early Detection of Breast Cancer
A study conducted by Alejandro Rodríguez-Ruiz, concludes that radiologists improved their cancer detection at mammography when using an artificial intelligence system for support, without requiring additional reading time.
Early Detection of Cardiac Diseases
Dawes et al. found that using artificial intelligence with a magnetic resonance imaging (MRI) algorithm for heart motion can help provide more accurate treatment for patients with high blood pressure and lung disease.
AI in Diabetes Prevention
AI-ML principles help create algorithms for predicting diabetes and its complications. AI enables remote monitoring of symptoms and biomarkers, empowering patients for self-management. AI is shifting diabetes care towards targeted, data-driven precision care, moving away from traditional approaches.
Public Health Surveillance and Response
Traditional public health surveillance relies heavily on statistical techniques. Artificial intelligence (AI) and Machine Learning (ML) technology have been widely applied to early warning and detection of infectious disease outbreaks. Recent years have seen tremendous growth in AI-enabled methods, including but not limited to deep learning–based models, complementing statistical approaches.
The research paper ‘Artificial intelligence–enabled public health surveillance—from local detection to global epidemic monitoring and control’, concludes that with the increasing interest and research happening in the domain of AI and public health surveillance, it’s clear that there’s a lot more to explore. Even though there have been some impressive achievements, it seems like this field is still just getting started, and there’s a ton of potential waiting to be unlocked.
Ethical and Regulatory Guidelines for AI in Healthcare
The integration of AI & ML into the medical field opens the immense potential of transforming every field of medicine. There will be No or lesser percentage of human error in diagnostics and radiography. Due to EHRs and digital devices, AI can early detect and prevent several diseases including cardiac, stroke, and diabetes.
However, it also raises the critical ethical and regulatory considerations. A research paper by Balaram Yadav Kasula tries to cover the ‘Ethical and Regulatory Considerations in AI-Driven Healthcare Solutions’. The study looked at the ethical side of things, focusing on topics like
- Patient privacy
- Keeping data safe
- Making sure algorithms are fair
- Being clear about decisions
- Being accountable
The paper also checked out how government policies and rules are changing to make sure AI in healthcare is used responsibly.
Through a comprehensive analysis, this study aims to shed light on the ethical dilemmas and regulatory gaps inherent in AI-driven healthcare, providing insights into navigating the ethical complexities while ensuring compliance with evolving regulations.
Responsible AI in Healthcare
Responsible AI in healthcare pertains to the deployment, conscious development, and various uses of AI technologies in the healthcare industries. AI in Healthcare is increasingly becoming pivotal as it has a lot of potential to enhance various operations in this industry from patient care to operational workflows, these technologies emphasize the principles of transparency, honesty, privacy, and security based on designing and implementation.
Principles of Responsible AI
AI Empowers human decision-making skills, but it is not meant to completely substitute it, as it is data-driven and these data and signals can be interpreted by scientists to make better and more acceptable decisions for patients. AI tools should be designed in a way to promote equity, this can eliminate biased decisions which are often based on stereotypes.
Privacy, transparency, and trust are crucial objectives in adapting to AI technologies in healthcare, there should be clarity in the collection and interpretation of data to the patients, families, and healthcare providers, this can build or destroy trust in AI Technologies. If patients feel that their data is being misused or the data provided is not authentic, then their confidence in these AI technologies in healthcare will drop drastically.
AI technologies should meet regulatory, ethical, and legal standards. Therefore, these technologies should be managed and evaluated regularly.
Challenges Faced
Adapting to AI healthcare solutions can be highly influenced by ethical and cultural factors. People living in rural areas still believe in primitive ways of solving health issues and refrain from the use of technologies, thus educating them about the various benefits and better authenticity on various health issues that AI could bring in should be explained with comparative study, extensive research and demonstration.
Geographical locations also affect the implementation of AI in healthcare especially between urban and rural areas, most healthcare institutions in rural areas have limited access to AI and these limitations affect the quality as well. These issues vary from place to place and are subject to change constantly, thus AI should be implemented after deep research and understanding of that particular region. These solutions should be tailored according to the region and the challenges faced.
Scope
AI in healthcare has brought unprecedented changes in organizations and in people, it has reshaped patients’ journeys and supported medical practices with accurate statistics, information, and unbiased evidence. Throughout the design, development, and implementation of AI in healthcare, ethical implications should be an uttermost necessity. Since technologies are evolving at a fast pace, it is not possible to apply ethical design as a one-time, standalone review procedure. As new issues arise, ethical impact evaluation needs to be a continuous, iterative process that can be repeated over time.
Education is a key aspect of responsible AI in healthcare, health practitioners and patients should be well-informed and taught about the various AI technologies, especially the ones serving or dealing with sensitive and high-risk contexts. This way trust and confidence in technologies build up and also increase transparency.
Opportunities
AI systems should be reliable and valid, this should be established through tests and experiments, thus gaining the trust and belief of users. These experiments or tests can also prevent many errors from happening. To mobilize various AI technologies in healthcare, users should understand the goals, scalability, and limitations of these technologies. AI healthcare systems should be monitored, examined, and evaluated regularly to prohibit inaccuracy and miscalculations.
For the improvement of AI systems, quality checks, and feedback assessments should be done. As AI is transforming the healthcare industry and is also growing drastically it should be used responsibly to safeguard users’ trust, and privacy, and should uphold medical ethics.
Cognitive Engagement with Responsible AI
Introduction
When we talk about Cognitive AI, what really comes to your mind? Now hold that thought and before we get to cognitive engagement, let’s first talk about Cognitive AI. So, in simple terms Cognitive AI is the concept that has been derived from AI and cognitive science. Its sole aim is to copy human behavior and solve complex problems. Human behaviors usually include human intelligence, memory, attention, knowledge formation, problem solving and decision making.
In cognitive engagement, 3 factors cognition, emotion, and motivation are incorporated into engagement. So, cognitive engagement in AI in healthcare includes information like the patient knowing about their disease, its treatment, developments, and monitoring.
AI tools are only making it easier to provide personalized treatment to patients and also increasing a patient’s cognitive engagement with those tools to make healthcare more accessible.
Additionally, as described by Graffigana et al. (2015) a patient’s engagement is a “process-like and multi-dimensional experience, resulting from the conjoint cognitive (think), emotional (feel), and cognitive (act) enactment of individuals toward their health management. In this process, patients go through four subsequent positions. blackout, arousal, adhesion, and eudaimonic project. The unachieved synergy among the different subjective dimensions (think, feel, act) at each stage of the process may inhibit patients’ ability to engage in their care.”
Factors Influencing Cognitive Engagement of AI in Healthcare
Regarding cognitive engagement in AI in healthcare, several factors influence it. A study conducted by (Dorothy Szinay, Olga Perski, Andy Jones, Tim Chadborn, Jamie Brown, and Felix Naughton 2021) revealed some of the major factors are knowledge, memory attention and decision processes, environmental resources, and peer and professional support. The study was conducted by them using the Capability, Opportunity, Motivation–Behaviour (COM-B) model and the Theoretical Domains Framework. Under knowledge, patients want instructions on how to use an app, statistical information, and health information.
Under memory attention and decision processes, patients wanted reminders and reduced cognitive load meaning the app should be easy to use and not time-consuming.
Under environmental resources, the patients wanted innovative features, adaptability and 2-way communication between the user and the app.
Under peer and professional support, the patients wanted a community for social interaction and an option to contact health professionals from within the app. The study also revealed several other factors like goal setting options, rewards, feedback, and encouragement.
Positive studies showing cognitive engagement in AI in healthcare
Using cognitive engagement in AI in healthcare has various positive outcomes one of which is perceived value. Several researchers have shown that perceived value and cognitive engagement are a good combination. Hence, with customer engagement in focus, several healthcare businesses are focusing on cognitive engagement with the help of AI for a sense of value creation.
Impact on Global Society
Breast Cancer and Predictions
AI utilization in the breast cancer prediction and diagnosis sphere has drawn many eyes, and a lot of studies have demonstrated its advantages. In a recent study by Naji et al. (2021), an AI model trained on mammogram images was discovered to have outperformed radiologists by predicting breast cancer risk in the next five years with significantly better accuracy. On the other hand, AI systems such as Watson from IBM have been built to help diagnose breast cancer by analyzing medical images and patient data; thus, maybe a more accurate and faster diagnosis could be possible.
Notwithstanding the encouraging progress that has been made, there are several critical issues. One of the most direct impacts of the incorporation of AI in the diagnosis of breast cancer is that it may worsen the existing healthcare disparities. Even though AI algorithms are good at improving accuracy, they need the data to be of high quality as it may not be accessible for all areas. This communication gap would have an enormous impact on breast cancer patient’s health care, and it could further widen the already existing discrepancies between breast cancer diagnosis and treatment.
Besides this, Patient privacy and consent are the ethical issues about AI in breast cancer prediction and diagnosis. AI algorithms use data from patients, and that is why there is a chance of data misuse and unauthorized access, which can result in the loss of patient confidentiality. However, AI is feared can lead to further infusion of biases in healthcare since AI algorithms are trained on historical data, which may reflect existing disparities in healthcare delivery and outcomes. This could contribute to treatment and diagnostic disparities, especially in the case of the underprivileged.
Finally, AI has the potential to improve the accuracy of the prediction and detection of breast cancer, but several crucial issues must be solved. These involve guaranteeing fair access to top-quality data for all people, protecting patient privacy and consent, and overcoming bias in AI algorithms. The usage of AI in breast cancer diagnosis must be done ethically and carefully so that the gains of such advancement can be spread to all patients.
Cardiovascular Disease (CVD)
Besides holding potential for the improvement of the course of cardiovascular disease (CVD) management, the integration of AI is being studied and examined critically. Similarly, predictive models driven by artificial intelligence, as established by recent research by (Townsend et al. 2022), demonstrate the potential to analyze electronic health record data to anticipate patients at elevated risk of CVD-caused complications, thus enabling the adoption of personalized efforts for prevention. Similar to the research done by (Fuchs and Whelton, 2020), it has been demonstrated that racism might exist in CVD prediction models, thereby showing the importance of training data to ensure the models are not biased against certain populations.
Additionally, diagnosis-taking is necessary for AI caution. On the leaps and bounds that have been made recently, AI tools, as demonstrated by the research done by (Fuchs and Whelton, 2020), have not been able to outperform healthcare professionals in detecting CV diseases, implying that there is a need to review and revise such tools. However, AI displays its potential to revolutionize CVD management only if it is combined with accurate data sets, measures taken for bias elimination, and a validation of generated findings against evidence-based standards. Rushing the deployment of AI tools before these issues are tackled might result in undesirable effects and achieve less than anticipated benefits.
Spread of Disease
Several AI-powered models have demonstrated a good record of predicting the spread of diseases such as COVID-19. The work of (Fan et al. 2020) exhibited an AI model for assessing the risk of COVID-19-positive patients becoming severe cases requiring hospitalization or death based on demographic and clinical data. The model had an elevated level of prediction accuracy in predicting the outcome for patients depending on their own characteristics and medical history.
Moreover, artificial intelligence has been applied to create predictive models for the spread of infectious diseases. Air traffic data analysis by machine learning algorithms showed its potential for the global prediction of the spread of infectious diseases. The model was indeed successful in its predictions of disease outbreaks such as H1N1 influenza and Zika virus, thus providing valuable insights to public health officials and policymakers. These innovations have great prospects of strengthening preparedness and response in the public health sector; nevertheless, some complexities must be scrutinized. First of all, the effectiveness of AI-based models of predictive accuracy depends on the level of quality and data availability. Erroneous or incomplete data are likely to yield wrong forecasts, thus rendering response activities futile.
The AI used for predicting the spread of disease also brings about the ethical consideration of touching privacy and consent. The models relying on a vast dataset that may contain sensitive information should be formed carefully as there could be a risk of data misuse or unauthorized access, which in turn may cause a breach of individual privacy.
Impact of AI on Marketing Disciple
AI is going to change the landscape of healthcare marketing techniques. It’s largely going to benefit forever future-looking pharma companies, healthcare institutions, hospitals, individual practicing doctors, and healthcare service providers.
Value-Centered Marketing in Healthcare
The healthcare segment is changing rapidly with the new digital technologies and Artificial Intelligence. A new approach to marketing in healthcare called value-centered marketing (VCM), which focuses on:
- Preferences – what patients want and need
- Precision – delivering the right care to the right person
- Process – making the care process smooth and efficient
Awesome latest Artificial Intelligence technologies right now making VCM even better. Healthcare professionals and service providers can collect tons of health data digitally, analyse it in new and powerful Artificial Intelligence technologies, and store it all easily. This lets healthcare companies combine the best of technology and marketing to create successful VCM strategies.
VCM is still at the amateur stage and healthcare professionals and service providers must use tech and data cautiously, but overall, VCM has the potential to be a game-changer!
Ollie Capel, founder of MEDICO Digital has covered the points in his article on ‘5 predictions on the future of AI in healthcare and pharma marketing’. It covers below points:
- Use of artificial intelligence for personalized patient engagement.
- Using large amounts of patient data for predictive analytics and data insights.
- 24/7 support by using AI-powered chatbots.
Doctor Sarah Roberts’s Case Study
Doctor Roberts, a renowned healthcare executive has transformed her organization’s marketing with AI, leading to impressive results and becoming a role model in healthcare. In her article, she stated that she used AI for patient personalization, predictive analysis, and best-personalized customer service. AI-powered healthcare marketing helped her transform her organization’s marketing.
By using artificial intelligence, healthcare organisations can personalise patient experiences, gain valuable insights, improve customer service, and optimise marketing efforts for greater impact.
Health Care AI-Powered Marketing Case Studies
Introduction
In the coming 10 years, Artificial intelligence with virtual reality and an increase in Digitization will transform the part of healthcare marketing in many scenarios related to the health sector. AI is proving to be one of the most important tools in changing the healthcare marketing sector, making it simple for marketers to reach their target audience proficiently. AI and machine learning currently play an important role in analyzing patient’s health data to predict their behaviors and requirements.
Healthcare professionals can generate personalized marketing campaigns, and understand patient’s unique preferences and requirements because of fast-paced AI technological advancement.
Patient Segmentation for healthcare marketing campaigns
In the healthcare sector, a huge amount of patient data is still unstructured or disorganized which makes it challenging to promote medical products and medications through personalized marketing campaigns. This issue has made healthcare industries embrace AI and machine learning solutions. AI has given authority to healthcare marketers to target the right set of audiences and transform their marketing strategies. Progressive algorithms are competent in analyzing huge chunks of data from varied sources like clinical trials, social media, and electronic health records (EHRs) to pinpoint niche audience segments.
Focussed targeting empowers marketers to provide relevant information at the right time, increasing engagement and sales for life sciences companies. By doing in-depth research, I found a few startups using AI to personalize marketing campaigns within the healthcare and life sciences sectors.
Deep Intent– Healthcare advertising platform tool that provides AI-powered deep insights and suggestions to tailor the marketing campaigns as per the niche targeting audience. Copilot assists in finding the ideal audience segments to enhance user quality while maintaining scale.
Semalytix– Develops solutions based on AI for pharmaceutical companies to understand their customer needs and preferences better. Their Pharos platform swiftly analyses a huge number of online posts and CRM data, assisting in creating focused marketing campaigns that serve the patient’s requirements.
AI-driven content marketing and creation
Life Sciences and healthcare content marketing techniques are drastically changing because of generative AI technology, which uses Natural Language Processing (NLP) and Natural Language Generation to create patient-centric content. These tools are helping marketers in generating high-quality engaging content that resonates with the patient’s pain points and requirements. Generative AI is proving competent in recognizing highly searched healthcare topics and keywords that align well with the targeting audience and satisfy the patient’s search intent by ranking well on search engines like Google.
To create patient-friendly educational material, medical health guides, blog posts, and explanatory articles about medicines, AI is proving to be a vital assistant in the healthcare industry. Healthcare and life sciences companies are using AI to generate content in real-world scenarios.
- The medical oncology department can leverage the power of AI to create educational guides on their cancer care websites, explaining cryptic medical processes and various cancer treatments in an easy-to-understand language.
- Pharma companies to explain the working of the new drug, they are using AI to swiftly analyze complex research papers and clinical studies to create content explaining the working of the new drug in the market.
- With AI-generated statistical insights and with a human touch, mental health websites can create blogs raising awareness about mental health issues and creating a helpful and sympathetic environment for the readers.
Enhancing patient experience with AI-powered chatbots
AI-powered chatbots are proving to be an emerging technology for healthcare and pharmaceutical companies. Even though the psychological AI chatbots are proficient in making visible improvements in delivering mental health care services they also possess some moral and technical challenges. A few crucial challenges include giving inaccurate information which can prove to be dangerous in medical situations, patient data privacy concerns where hackers can access the private medical data which can put the hospitals at risk of a lawsuit or hefty fines, and lack of governance and regulatory policies to tackle the conventional risks that such technologies may cause to the companies implementing them.
A New York based digital health startup known as K-Health is working on developing an AI-powered chatbot app that will consume patient’s symptoms and medical historical data to suggest medical conditions based on the comparison with the data of millions of patients. Currently, K Health’s technology is working as a digital assistant. Allon Bloch, co-founder and CEO, of K Health stated that ‘Doctors spend a lot of time collecting information from forms asking basic questions that machines can do’
Used Cases of Conversational AI
-Appointment Scheduling: A smart conversational AI platform can quickly schedule, reschedule, or cancel appointments as per the patient’s requirements. Hence, reducing hand-operated inputs and human mistakes.
-Patient Care Management: Conversational AI works as a vital medium between healthcare professionals and their patients. With the help of this technology, patients can easily avail access to their test results, and request their medication details.
-Seamless Bot to Agent Hand-off: Human touch is always valuable when it comes to handling delicate areas like healthcare. Conversational AI is trained to assess the patient’s needs and way of thinking. AI chatbots are efficient in transferring complex conversations to human agents so that patients are not served with misinformation about their health concerns, medications, and so on.
Reference List
https://www.linkedin.com/pulse/artificial-intelligence-effective-healthcare-marketing-strategy
https://www.techtarget.com/searchenterpriseai/tip/Pros-and-cons-of-conversational-AI-in-healthcare
https://www.medicodigital.co.uk/ai-pharma-healthcare-marketing-predictions
https://www.deepintent.com/the-value-of-pre-optimizing-your-healthcare-audience-segments
https://www.wsiworld.com/blog/how-ai-is-revolutionizing-healthcare-a-digital-marketing-perspective
https://searchbusinessgroup.com/healthcare-content-creation-spotlight-on-the-power-of-ai