Artificial intelligence (AI) can help patients learn more about their conditions and help them make decisions about their diagnosis and treatment. That is the message some healthcare startups are seeking to share with people and practitioners about AI’s potential.
Hype fuels trends and innovation, and is often associated with technological tools in healthcare, including AI. Yet, recently, particularly with the rise of ChatGPT, there are increasing efforts to make information available to patients in a more accessible way. With the focus on democratising healthcare, AI’s potential to strengthen patient care and resource management for practitioners creates hope. The question is: Can AI truly empower patients?
Determining AI’s role
Developers have crafted AI tools to match patients to clinical trials within the healthcare industry. However, with its ever-advancing nature, insiders are asking whether AI is an ethical means to help patients and whether it can authentically support healthcare diagnostics and treatment.
In the 2021 study, the researchers found that people are reluctant to implement AI in a medical setting due to the subjective belief that it is challenging to understand associated algorithms and human medical decision-making. People were hesitant to use medical AI, despite finding it is a cost-effective and scalable solution that can outperform human practitioners.
Regarding subjective knowledge, the researchers suggest patients think they understand how human doctors make decisions, but not how AI makes similar decisions. “In reality, patients’ objective knowledge is equally low for human medical decision-making or AI decision-making,” adds study author Romain Cadario, Assistant Professor of Marketing at Erasmus University, Rotterdam School of Management.
Replicating the human connection
As per GlobalData, pharma companies have been innovating on the AI front, having filed 607 AI-related filings by entities in this sector in the last six months. GlobalData estimates the total AI market will be worth $383.3 billion in 2030, implying a 21% compound annual growth rate between 2022 and 2030.
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Additionally, startups are also developing AI tools are communicating their potential in enhancing healthcare.
DataRobot, an AI platform strives to deliver intelligence and enable access to enhance patient empowerment. “Data and the ability to get insights from that data at scale are highlighted as key requirements for the democratisation of healthcare,” says Sally Embrey, Field chief technology officer in Healthcare at DataRobot says, referencing the Stanford University of Medicine report findings.
Along with patient-centred care, DataRobot explores the need to focus on the individual before they become a patient. “It is a person being able to take ownership of their end-to-end health because they are empowered to access data,” says Embrey.
However, despite the growing possibilities with AI, people value their doctor-patient relationship. “Previous research showed that patients are more familiar with medical doctors,” says Cadario. The familiarity results in patients feeling that medical doctors are more susceptible to considering their unique patient characteristics and that they can be held accountable, he adds.
Can AI truly understand?
Pharmaceutical manufacturers utilise intelligent virtual assistants that can support patient access to a range of treatments, medication information and education, support medication adherence, symptom assessment and triage, clinical trial programmes and financial assistance programmes. Companies position these as enabling a more efficient and convenient platform for patient engagement.
Commenting on how we can expect AI to help patients understand more about their diagnosis and treatment, Brian Anderson, Vice President and General Manager at Amelia, an enterprise conversational AI provider in healthcare, says: “Intelligent virtual assistants can communicate empathetically at a level of patient understanding that helps them access information about their diagnosis and treatment options.”
Rather than seeking to help patients gain greater insights into their diagnosis and treatment, some may argue AI’s influence is on democratising healthcare. “I am not sure AI is about ‘understanding more’ rather than wider access,” Cadario.
For example, AI has potential in settings such as when a patient lives in the countryside and therefore finds it hard to find a dermatologist. AI can help deliver a quick triage decision to decide whether they need to consult as soon as possible, Cadario shares.
Improving access to healthcare
As ChatGPT becomes popular in the healthcare industry’s vocabulary, talk turns to how conversational AI supports patients in accessing information and education about their health. “Conversational AI plays a large role in supporting patients within various areas of their healthcare journey,” says Anderson.
Call centres, websites and portals are among AI assistants’ capabilities to augment traditional channels. Today, patients expect self-service capabilities that allow more accessible communication in their preferred language and engagement over various channels, Anderson shares. Examples of these include chat functioning, inbound and outbound voice, text, and email to support disease and therapy management.
“Overall, the healthcare industry has been relatively slower in adopting AI,” says Anderson. “However, the introduction and accessibility to Large Language Models have accelerated different AI technologies like conversational AI,” Anderson details.
Healthcare automation company Notable launched its Patient AI tool in April 2023, using Large Language Models and the technology behind ChatGPT (GPT) to scale personalisation in healthcare. The tool reviews email records and third-party data to obtain millions of data points to develop social and clinical patient understanding. The tool automatically translates these into personal patient recommendations.
“Large Language Models represent a significant advancement in the field of AI,” researchers in a 2023 study state, regarding their role in education, particularly in natural language processing (NLP). Developers train the models on extensive amounts of text data to generate text, answer questions and complete language-related operations that mimic human output.
While advancements to the tech develop, ethical issues remain within the medical AI field. “Any healthcare organisation looking to implement AI must carefully navigate concerns around patient consent, regulatory and governance issues, and data security and privacy,” says Anderson. “It is clear that we are at a turning point as it relates to the convergence of the practice of medicine and the application of technology,” researchers of a 2021 study into AI concluded. Over the next ten years, AI in healthcare will centre on how the tech can contribute to better clinical outcomes and develop data assets and tools. However, translational research in AI healthcare applications is vital to overcome obstacles and pursue opportunities.