From expanding access to care to preventing the spread of emerging infectious diseases, learn how AI in healthcare is transforming the sector.
Over the past decade, artificial intelligence has become a ubiquitous part of life. From AI chatbots helping consumers find products to complex machine learning algorithms that help drive the success of entertainment platforms like Netflix, AI is everywhere. And as the healthcare sector continues to evolve, AI has a tremendous amount of potential to help shape the future of the space for good—if deployed properly. While the possible applications of AI in healthcare are unendingly vast, there are several uses that are sparking massive conversations and driving investment right now.
The potential of AI in healthcare
Under the umbrella term of ‘AI’, there are a variety of different technologies at play in shaping the future of our healthcare systems, from machine learning to deep neural networks. Here are just a few ways in which artificial intelligence could improve the care people are already receiving:
Expanding access to care
One of the biggest conversations happening now is how to get more people access to better care. While the care gap had been widely documented prior to 2020, the pandemic made it much clearer that certain communities were more privileged than others in terms of access to affordable, reliable, and quality healthcare.
Especially in developing nations, severe deficits of trained clinicians and technical experts like radiologists make it much harder for people to receive life-saving care. Through AI imaging tools, artificial intelligence software can help take care of diagnostic tasks that are typically done by human technicians. Especially in areas with little access to resources, these AI tools could help reduce the care deficit.
Precision medicine
One of the most common applications of machine learning in the healthcare field as of now is precision medicine. This is a practice that helps clinicians predict which treatments will be the most likely to succeed on patients based on the individual patient’s attributes as well as the context of their specific treatment. While this practice has been used in medicine for some time, greater advances in machine learning will allow healthcare providers to utilize more complex forms of this AI—like deep learning and neural networks to be able to better predict patient outcomes and risk factors.
Natural language processing
Being able to parse through and make sense of human language has been a goal of AI since its inception in the 1950s. And while the technology has made great progress since then, natural language processing (NLP) still has a long way to go. In healthcare specifically, NLP could work alongside doctors to help create and classify clinical documentation. Along with understanding unstructured clinical notes on patients, this type of AI could help write reports and transcribe patient/doctor interactions as well.
Improving the Electronic Health Record
While Electronic Health Records (EHRs) are a great resource for patient data, issues around data quality and integrity, along with disparate formats and structures, have long made it difficult for care providers to gain meaningful insights from these databases. Deep learning can help healthcare systems better categorize and organize this data, helping clinicians predict patient risk factors and take appropriate steps to mitigate those risks.
Mitigating antibiotic resistance
Antibiotic resistance is a growing concern among the health field. “Superbugs” that are resistant to multiple types of drugs can cost billions of dollars to fight each year and kill thousands. Where AI can come in is through organizing and parsing through EHR data. This data can help identify infection patterns and flag patients at risk of becoming ill before they begin to show symptoms.
Wearables
A large topic of discussion in health is preventative care, and how to help patients monitor and take control over their own health. Wearable devices like fitness watches and smartphones can already collect a treasure trove of health data on individual patients, but many doctors need better AI support to be able to understand and draw insights from these pools of data. As more people become comfortable sharing this health data with their care providers, this could not only improve patient outcomes, but save patients and hospitals billions of dollars each year.
There are endless applications of AI in healthcare; the decision comes down to private companies prioritizing innovation.
Ethics and AI in healthcare
While there are a myriad of ways the future of AI in healthcare could help patients and care providers, global bodies like the WHO warn that AI technologies will have to be built with ethics and human rights in mind in order to avoid bias and potential misuse. In order to help experts create human-centric AI, the WHO issued 6 principles of design meant to help prevent and minimize risk.
1. Protecting human autonomy
Privacy and patient autonomy are of critical importance in the healthcare system. In terms of AI, algorithms and technologies should be built with patient confidentiality in mind at all times. What’s more, healthcare providers must ask for informed consent before utilizing these technologies in individual patient contexts.
2. Promoting human well-being and safety and the public interest
At their core, AI tech in healthcare is meant to keep patients safe and healthy. And as such, designers of these technologies need to be able to meet regulatory requirements for safety, accuracy, and efficacy as laid out by peer-reviewed use cases. Additionally, all AI built needs to be measured via continuous quality control.
3. Ensuring transparency, explainability and intelligibility
AI is meant to help people better understand their health and the treatment options available to them. As such, information on AI systems needs to be published and documented before its use. Transparency between care providers and patients needs to be prioritized, and information on the AI should be readily available and accessible to patients.
In addition, information on these systems should be widely available to the public as well in order to create meaningful conversations about their use and quality.
4. Fostering responsibility and accountability
While AI can do many things that human caregivers cannot, it’s still the responsibility of these providers and other stakeholders to make sure each technology is used as it’s meant to be used. In cases where there is a question of malpractice or misuse, there needs to be systems in place where patients and other groups can advocate for investigations and redress for those negatively affected by decisions made by algorithms or medical malpractice.
5. Ensuring inclusiveness and equity
The best outcomes come when AI is designed with all communities in mind. AI systems will have to be built using data from a wide variety of ethnicities, sizes, genders, environments, and more to ensure the most equitable outcomes. And this inclusivity must be extended to its usage and accessibility.
6. Promoting AI that is responsive and sustainable
AI in healthcare is only as good as its ability to meet expectations. Designers should work to ensure that these technologies are consistently tested to ensure its responsiveness and ability to meet patient and doctor expectations. Additionally, these systems should be designed with environmental sustainability in mind, and work to improve things like energy efficiency and resource management.
What’s next for AI in healthcare?
The future of AI in healthcare may seem promising, but the next steps lie in the hands of healthcare startups, private healthcare providers adopting innovative technologies, and larger-scale roll-outs to truly revitalize the industry. Research, proven data, and peer-reviewed use cases on these technologies will be necessary to prove AI’s effectiveness—and safety—in providing human patient care.
If you’re interested in learning more about what AI is already being adopted in the healthcare industry, we’d be happy to connect you to our deep network of affiliate AI companies. Contact us today to learn more.
Comments