There are many possible uses of AI and ways to improve processes within the healthcare field.

Firstly, AI can help to provide significant data-driven support to healthcare workers. By using data algorithms, technologies can identify patterns and deliver automated insights for common applications like health monitoring, managing medical records, treatment design, and digital consultations. 

With less time being spent on repetitive administrative tasks, medical professionals can deliver better care while having a less-demanding number of tasks to complete. Additionally, this could alleviate clinician burnout, which has been a critical issue, especially since the start of the COVID-19 pandemic. There are several ways AI can be integrated into the daily workflows of healthcare providers.

“A few broad examples would be the automation of routine administrative tasks, freeing up time for healthcare providers to focus on patient care, and analysis of large amounts of medical data quickly, accurately, and efficiently, which can lead to faster and more accurate diagnosis and treatment,” Brockett said.

Currently, diagnostic errors account for 60% of all medical errors and an estimated 40,000 to 80,000 deaths each year. According to a study, around 12 million people in the United States are misdiagnosed annually, and 44% of those are cancer patients. 

AI is helping overcome this issue by improving diagnostic accuracy and efficiency. However, although AI can offer more accurate diagnostics, there’s always a chance that it can make mistakes, which causes some companies to hesitate about adopting AI for diagnosis.

Another area for huge innovation is the training of AI to interpret medical images such as X-rays, CT scans, and histopathology slides to identify abnormalities, lesions, and tumors. AI-provided automated diagnosis can also make diagnoses faster and more accurate.

Thus, the development of new AI programs is actively redefining radiology, especially since AI has the ability to extract data that is not visible to the human eye. While AI can be more skilled than radiologists in analyzing most medical data, it’s not yet mature enough to completely replace radiologists. 

The Massachusetts Institute of Technology (MIT), for example, has created an ML system based on a hybrid approach that can diagnose different types of cancers by analyzing medical reports or referring the task to an expert radiologist.

In addition, AI can be used in several other ways to improve pathology in healthcare.

“AI can help identify patients at risk of developing certain diseases, such as specific types of cancer, and could enable earlier intervention to possibly prevent occurrence or worsening of a specific disease. AI can also help to identify the most effective treatment plans for each individual patient based on their unique characteristics, resulting in better outcomes and improved quality of life,” Brockett said.

For instance, AI can be used to provide clinical decision support, assisting pathologists in making accurate diagnoses and treatment decisions. It can also be used to analyze large volumes of pathology data, including patient records, medical images, and genomic data, to identify patterns and insights that can inform future research and treatment strategies.

AI can also be used to ensure quality control in pathology labs, detect errors, and identify areas for improvement.

Additionally, AI can drastically improve drug development as well as the clinical testing associated with it.

AI can be used to analyze data to identify potential drug targets, design new molecules with specific properties, find existing drugs that could be repurposed for new indications or conditions, and predict drug efficacy, toxicity, and adverse effects. All of these processes would significantly reduce the time and cost of preclinical testing and clinical trials.

Thus, AI would optimize clinical trials by identifying suitable patient populations, predicting patient recruitment and retention, and monitoring patient safety and progress during the trial.

“Many companies are leveraging AI to help in patient recruitment for clinical trials by identifying eligible patients and matching them with enrolling studies, which would help expedite the overall trial and get therapies to the patients who need them sooner. AI is also being leveraged to improve the accuracy of trial endpoints. A failed trial due to inaccuracy of endpoint assessment wastes a tremendous amount of resources and significantly delays approval of therapies for patients who need them,” Brockett said.

AI can analyze patient data, such as genetic information and medical history, to predict an individual’s response to a particular drug and develop personalized treatment plans. Patient outcomes could show great improvement through these methods of medicine personalization.

Within the healthcare sector, there are many departments with varying focuses. So, naturally, there are plenty of additional ways AI can impact healthcare.

However, there’s one more highly important aspect of healthcare that AI can change. Many Americans see promise for artificial intelligence to help issues of bias in medical care based on race or ethnicity. In fact, Americans who are concerned about this bias are more optimistic than pessimistic about AI’s potential impact on the issue.

According to a recent research survey, 64% of Black adults say bias based on patients’ race or ethnicity is a major problem in health and medicine. And over half of the people concerned with this issue, 51%, believe that relying on AI could be an effective way to solve the problem.

Overall, it’s evident that utilizing AI in healthcare has the potential to be vastly beneficial. Despite this, there are still many challenges that may hinder its implementation.

Although AI is driving important advancements in healthcare, there are still barriers that are slowing its incorporation into the industry.

“Integrating AI into healthcare systems comes with several challenges, such as the need for standardization of data and the development of algorithms that are explainable and interpretable,” Brockett. “To overcome these challenges, it is essential to have collaboration between healthcare providers, researchers, and policymakers to develop guidelines and regulations that ensure the safe and effective use of AI in healthcare.”

Firstly, there are important ethical issues to consider when aiming to achieve the full potential of AI in healthcare.

This includes addressing the use of patient data, safety and transparency, algorithmic fairness and biases, and data privacy.

Privacy regulations can make it difficult to collect and pool healthcare data. With especially strong privacy concerns in health care, it could be too difficult to use real, high-quality health data to train AI models as quickly or effectively as in other industries.

“Privacy of personal health information is a tremendous concern and one that should never be taken lightly. In that same vein, there can be a tendency for machine learning algorithms to be biased based on the training information they receive. Therefore, there are ethical concerns around the under-representation of certain patient populations in training data. These concerns extend to the entire healthcare field, not just AI,” Brockett said.

Additionally, the regulatory approval process for new medical technologies takes some time, and the technology is reviewed meticulously. Innovations can take years to undergo the approval process.

Healthcare providers may also hesitate to adopt new technology for fear of law implications so that liability concerns may be a barrier.

In terms of technical limitations, AI algorithms require a lot of computing power, storage capacity, and specialized hardware and software, which can be a challenge for healthcare organizations with limited IT resources and infrastructure.

Furthermore, healthcare data is often put into different systems and formats, potentially hindering the deployment of AI algorithms that rely on large and diverse datasets. 

“To overcome these obstacles, healthcare providers must have confidence in the AI tools they use and ensure that they provide accurate and reliable information,” Kim said. “AI tools must be compatible with existing healthcare systems and workflows, which can be a complex and time-consuming process. At Stanford AIMI, we address these challenges by developing, validating, and disseminating AI tools based on rigorous science.”

Moreover, the actual adoption and integration of AI is difficult when it requires significant organizational and cultural changes, including training and education of healthcare professionals and changes in clinical workflows and decision-making processes. 

Of course, people on the receiving end of healthcare services must be comfortable with the use of AI as well. This presents a further string of challenges.

According to Pew Research Center, about 60% of Americans would be uncomfortable with a provider relying on AI in their own health care.

People are typically resistant to change, especially when it comes to healthcare. When new technology is presented, it can create hesitations. Thus, patient reluctance is a major barrier to implementing AI in healthcare.

Generally, the real-world use of AI is a challenge in itself. For example, almost none of the ML tools recently developed to tackle COVID-19-related problems had a very significant impact.

Overall, while AI has the potential to revolutionize healthcare, its integration presents several challenges that need to be addressed to ensure its safe and effective use in patient care.

In 2023, there’s still a long way to go to achieve AI’s full potential in healthcare. Looking forward, it’s clear that AI will continue to play an increasingly important role in the healthcare industry.

“I think using AI will be a great way to enhance the areas of healthcare and medicine over time, but we first need to make sure that it’s completely safe and accurate when performing important tasks. Overall, it seems like AI is going to really shape the future of healthcare and our understanding of it,” said Ashwika Narayan, a sophomore at Carlmont.

Currently, countless healthcare companies, institutions, and providers are working to design and implement AI that will improve various aspects of patient care. These include Path AI as well as the Stanford AIMI Center.

“The AIMI Center draws on Stanford’s expertise in clinical medical imaging, bioinformatics, statistics, electrical engineering, and computer science to develop, evaluate, and disseminate AI systems that benefit patients at Stanford and worldwide. Our interdisciplinary research in artificial intelligence optimizes the use of clinical data to promote health and solve clinically significant imaging problems,” Kim said.

As AI continues to evolve, experts expect to see even more advanced applications and solutions emerging, making healthcare more efficient, effective, and accessible to people around the world. While there are still challenges to overcome, the future of AI in healthcare is bright, and we can look forward to seeing the positive impact it will likely have on the lives of patients and healthcare providers alike.

“As a leader in this field, I am particularly interested in the role of AI in diagnostic excellence across various medical specialties, the societal impact and fairness of AI, open data and science, and the clinical implementation and translation of AI solutions,” Kim said. “Like many others, I am passionate about using AI to improve patient outcomes and drive progress in healthcare delivery.”