The healthcare industry is constantly evolving, with new technologies and treatments being developed daily to improve patient outcomes. At the forefront of change in the healthcare ecosystem is the use of massive amounts of data to drive real-time intelligence through such technologies as AI, ML, NLP, and generative AI. As a result, both HIMSS and ViVE made these technologies a core theme for this year’s conferences, with one key focus being artificial intelligence (AI). AI can transform how healthcare is delivered.   

With the rise of AI, healthcare organizations are establishing Centers of Excellence (CoE) that prioritize clear governance, well-defined processes, and training to ensure AI’s ethical, safe, and effective implementation. Today we will explore how AI is being used in healthcare while highlighting the necessary steps to ensure success in the implementation of AI within your organization. AI is changing the entire healthcare landscape from the importance of CoE to the role of people and processes. 

 1. Develop a Center of Excellence:  

Developing a Center of Excellence (CoE) with clear governance and the right roles is critical for managing a successful and evolving AI strategy. A CoE enables the consolidation of AI expertise within a single unit, allowing for the development of specialized skills and knowledge critical for implementing any AI strategy. It also provides clear governance and oversight, ensuring the technology is used ethically, safely, and effectively. 

With a CoE, collaboration between different departments within an organization is facilitated, allowing for the development of cross-functional teams that can work together to implement AI solutions aligned with the organization’s goals and priorities. By having a CoE with clear governance and the right roles, healthcare organizations can achieve immediate ROI by implementing AI solutions that are aligned with their business objectives. This helps healthcare organizations leverage the power of AI to improve patient outcomes, reduce costs, and enhance the quality of care. 

 2. People, Process and Training:  

Developing a CoE involves not just acquiring the right technology and tools but also establishing a culture that supports the adoption and scaling of AI. This requires a focus on people, processes, and training. The people involved in the CoE play a critical role in its success. This includes not just the dedicated team responsible for implementing the AI strategy but also stakeholders across the organization who will be impacted by the changes brought about by AI. Effective communication and collaboration with all stakeholders are crucial for gaining buy-in and ensuring that everyone understands the goals and benefits of the CoE. 

Once the people are in place, the CoE should have clear processes for selecting, implementing, and monitoring AI solutions. These processes should be aligned with the organization’s strategic goals and priorities, as well as regulatory requirements. For example, it should have established protocols for addressing any ethical and privacy concerns that may arise. And finally, the CoE should provide training to both the AI team and the broader organization on how to use and interact with AI tools and technologies. This training should cover technical aspects of AI, as well as the potential impact of AI on the organization and the broader healthcare ecosystem. It’s also important to address any concerns or misconceptions about AI and provide guidance on how to use it effectively. 

3. Diagnosis and Imaging:  

One of the primary areas where AI is being used in healthcare is diagnosis and imaging. AI can analyze medical images, such as X-rays, CT scans, and MRI scans, to identify and diagnose medical conditions. In addition, machine learning algorithms are now being trained to detect abnormalities and predict disease progression, providing physicians with accurate and timely diagnoses. 

For example, researchers used deep learning algorithms to analyze chest X-rays and detect pneumonia in a study published in the Journal of Radiology. The algorithm identified pneumonia with an accuracy of 92%, compared to a radiologist’s accuracy of 87%. Another study published in the journal Nature Medicine used deep learning to analyze retinal images and detect diabetic retinopathy with an accuracy of 94%. 

AI is also already used to analyze medical images to detect early signs of diseases such as cancer. By detecting cancer early, doctors can provide more effective treatments and improve patient outcomes. Google’s DeepMind Health, for example, developed an AI system that can detect breast cancer in mammograms with an accuracy rate of 94%. 

4. Personalized Medicine: 

Another area where AI is being used in healthcare is personalized medicine. Using algorithms to analyze large amounts of data, AI can identify patterns and develop customized treatment plans tailored to a patient’s unique needs. For example, AI can help identify patients’ most effective treatment plans by analyzing their genetic and medical history.  

Moreover, platforms like Cyrano.ai are leveraging AI to improve patient care by analyzing conversations or transcripts to produce real-time analyses.  

Other examples of AI treatments can be seen in heart disease and cancer studies. The journal Lancet Digital Health recently published about researchers who used machine learning algorithms to develop a personalized treatment plan for patients with heart disease. First, the algorithm analyzed the patient’s medical history, lab results, and imaging data to predict their heart attack and stroke risk. Then it developed a personalized treatment plan that included lifestyle changes, medication, and surgery. 

And in a study published in Nature, AI is being used to develop personalized cancer treatments. For example, researchers used AI to analyze the genetic data of patients with ovarian cancer. As a result, the algorithm was able to identify genetic mutations that could be targeted with specific drugs, resulting in improved patient outcomes. 

5. Virtual Health Assistants:  

Chatbots can be used as virtual health assistants to provide patients with medical advice and guidance. Patients can ask questions about their symptoms, medications, and treatments and receive accurate and personalized responses. 

These virtual health assistants are helping patients manage chronic conditions such as diabetes and asthma. For example, in a study published in the Journal of Medical Internet Research, researchers developed a chatbot that provided patients with personalized diabetes management advice. The chatbot used machine learning algorithms to analyze the patients’ glucose levels, medications, and lifestyle factors to provide personalized advice. 

These virtual assistants can also help patients access healthcare services more efficiently. Another example came in a study published in the Journal of Medical Internet Research, where researchers developed a chatbot that provided patients with information about healthcare services in their area. The chatbot used natural language processing (NLP) algorithms to understand patients’ queries and provide accurate and relevant information. 

 6. Medical Records and Billing: 

 AI is also already being used to automate medical record-keeping and billing processes, reducing errors and improving efficiency. Machine learning algorithms can extract data from medical records, reducing the time and effort required to review and analyze patient data. 

For example, researchers developed a machine learning algorithm that could automatically extract data from electronic health records (EHRs). The algorithm accurately identified patients with chronic kidney disease, updating their medical history records and predicting their risk of developing kidney failure. 

And it has further been shown that when AI automates medical billing processes, it reduces errors and improves efficiency. The Journal of Medical Systems displayed this by developing an AI system that could automatically code medical diagnoses and procedures for billing purposes. The system could accurately code 97% of diagnoses and 94% of procedures, reducing the time and effort required for medical billing. 

 7. Drug Development:  

AI accelerates the drug development process by identifying new drug targets, predicting the efficacy of potential treatments, and speeding up clinical trials. Using algorithms to analyze large datasets, AI can help pharmaceutical companies identify new treatments and bring them to market more quickly. 

For example, in a study published in the journal Nature, researchers used AI to identify a new drug target for Alzheimer’s disease. The algorithm analyzed large datasets of genetic and protein data to identify a protein that plays a crucial role in the progression of Alzheimer’s disease. This discovery could lead to the development of new treatments for the disease. 

AI can also predict the efficacy of potential treatments before they are tested in clinical trials. For example, in a study published in Nature Communications, researchers used machine learning algorithms to predict the efficacy of cancer drugs. The algorithms analyzed genetic and drug data to predict which patients would respond best to certain treatments. 

Conclusion: 

As we have seen at both HIMSS and ViVE, AI has enormous potential to improve the healthcare industry by providing faster and more accurate diagnoses, personalized treatment plans, and more efficient and effective healthcare services. From diagnosis and imaging to personalized medicine, virtual health assistants, medical records and billing, and drug development, AI is transforming how healthcare is delivered. As AI continues to evolve, we can expect to see even more innovative uses of this technology in healthcare.