AI in Healthcare: 5 Best Practices for Safe and Effective Implementation


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AI in Healthcare: 5 Best Practices for Safe and Effective Implementation

In 2017, a global trend study revealed that 86 percent of healthcare providers and life science companies use AI daily, proving its usefulness and efficiency.1 As technology continues to evolve and ingrain within our society, it’s unsurprising that it continues to rise in various industries, including the medical field.  

However, AI can also harm healthcare systems and patient care depending on its utilization, implementation, and regulation. If you want to maintain integrity and security within the healthcare industry, it’s essential that you learn the best practices to safely and effectively implement AI.  


Artificial Intelligence in the Healthcare Industry 

AI is rapidly transforming the healthcare industry, and professionals are now using the latest tools to improve outcomes, streamline operations, and accelerate the rate of medical breakthroughs.  

Data shows that the global artificial intelligence in the healthcare market was valued at $16.3 billion in 2022 alone.2 This reflects AI’s widespread adoption and integration across different healthcare domains. According to industry projections, AI in healthcare will continue to grow with a compound annual growth rate of 40 percent and is expected to reach a market value of around $173 billion by 2029.  

This exponential growth trajectory highlights the increasing recognition of AI’s capabilities in addressing complex healthcare challenges and driving innovation within the sector. Here are some of the benefits of AI: 


1. Enhanced Diagnostic Accuracy 

AI algorithms, such as X-rays, MRIs, and CT scans, can analyze medical images with remarkable accuracy. By identifying subtle patterns and abnormalities that human clinicians may overlook, AI can improve diagnostic accuracy and facilitate earlier detection of diseases. This enables healthcare professionals to initiate timely interventions and improve patient care.


2. Predictive Analytics 

Powered by machine learning, you can program AI to predict patient outcomes and potential disease progressions. It can also identify individuals at high risk of developing certain conditions or comorbidities. 

By analyzing patient data like medical history, genetic information, and lifestyle factors, AI can assist in developing personalized treatment plans and preventive care strategies. This proactive approach to healthcare management can help improve overall patient well-being.  


3. Clinical Decision Support 

AI-powered systems can provide healthcare professionals with real-time insights and evidence-based recommendations that are helpful in treating their current patients. Through analyzing medical literature and clinical guidelines, AI can provide summarized data that professionals can use throughout their decision-making process. 


Risks and Challenges of AI Implementation 

While AI offers immense benefits, it also presents significant concerns that must be addressed responsibly. Here are some of the critical challenges you need to be mindful of as a medical professional: 


1. Increased Bias and Discrimination 

AI’s algorithms and machine learning capabilities rely on historical data. While it helps AI familiarize itself with medicine, prevention, diseases, and its principles, it can still perpetuate biases from the past.  

For example, a study from the Stanford School of Medicine found chatbots responding to medical queries with racist ideas, like conducting physical assessments based on incorrect assumptions and false information regarding lung capacity, skin thickness, pain tolerance, and brain size.3 


2. Generation of Dangerous Predictions 

AI systems are powerful computational tools, but their predictions and recommendations aren’t always accurate. Al algorithms can generate flawed outcomes if not adequately trained, validated, and monitored. This creates risks of incorrect diagnosis or inappropriate drug recommendations, leading to severe consequences for patient safety and causing complications or adverse side effects. 


3. Ethical Challenges in Accountability 

When an AI algorithm makes mistakes like recommending incorrect treatment plans, it becomes challenging to pinpoint who is responsible.  

Many organizations use AI, but most companies have not established clear ethical frameworks and guidelines. This creates problems around transparency and trust between institutions, professionals, and patients. 


4. Risks in Data Privacy and Security 

AI integration in healthcare relies heavily on collecting, storing, and processing vast amounts of sensitive patient data. 

Although this gives professionals quick access to information, it also introduces risks of cyberattacks and unauthorized access. This puts valuable data vulnerable to identity theft, personal data breach, discrimination, or even physical harm if misused.  


5 Best Practices for Employers 

Private and public sectors need to create more stringent policies and governance to prevent AI-related healthcare issues nationwide.  

As an employer, you can contribute to the safe and effective implementation of AI in healthcare. Here are some of the best practices you can use to minimize risks and challenges in using AI: 


1. Prioritize data and security. 

Employers should prioritize the security and integrity of data used in AI applications. This is crucial since AI systems in the healthcare domain rely on sensitive and confidential patient data for training and operation. Here are a few cybersecurity measures you can implement: 


Data Encryption 

This method converts data into a coded format that can only be accessed and read with a specific password or decryption key. This helps protect patient data during transmission and storage. Even if unauthorized parties gain access to your data, you can rest assured that your information stays secure and unreadable. 


Strict Access Controls 

Consider granting role-based access and only allowing limited people to access your data. You can set a multi-factor authentication mechanism to prevent unauthorized access or misuse of data.  


Robust Security 

You can implement extensive firewalls to block people trying to illegally access your organization’s data or establish intrusion detection and prevention systems to further secure your data.  


2. Ensure ethical use of AI algorithms. 

Create bias-free algorithms and ensure this is implemented well in the AI system. You can do this by carefully selecting training tools for your AI and employing professionals from diverse backgrounds. Focus on eradicating information sets that perpetuate biases related to race, sex, religion, age, etc. It’s also necessary to double-check your data to ensure it represents a diverse population.  

Ensure ethical usage by prioritizing fairness and equality. Establish ethical guidelines and principles that can help govern the development, deployment, and usage of AI tools. Specifically, you can create rules that recognize ethical problems before they arise.  


3. Invest in comprehensive training programs. 

Ensure that your healthcare professionals have the knowledge and skills necessary to use AI in their roles effectively. You can do this by investing in comprehensive training programs discussing AI fundamentals and its potential uses in healthcare. 

To achieve further proficiency, you can collaborate with AI vendors, academic institutions, or professional organizations to develop tailored training modules that cater to your workforce’s specific needs and preferences. Additionally, hands-on training sessions, workshops, and simulation exercises can help healthcare professionals gain practical experience and confidence in using AI tools and applications.  

Here are a few training programs and certifications your medical professionals can pursue: 


Certified Health Informatics Systems Professional (CHISP) 

This certification program expounds on topics like healthcare analytics, artificial intelligence, and machine learning in healthcare informatics 


Certified Professional in Healthcare Information and Management (CPHIMS) 

Modules under this program focus on improving a professional’s knowledge of AI applications, data analytics, and predictive modeling within the medical field 


Machine Learning for Healthcare 

Offered by Stanford University, this course would provide an in-depth exploration of machine learning techniques and how they’re used to provide healthcare for patients. 


Clinical AI Certificate Program 

Offered by MIT Professional Education, it teaches professionals about deep learning, natural language processing, and ethical considerations when using clinical AI. 


4. Validate AI solutions and methods. 

This involves conducting rigorous testing, validation, and peer review processes to assess the performance and effectiveness of AI algorithms across diverse datasets and use cases. Also, stay informed of emerging validation methodologies and regulatory requirements to ensure you comply with best practices. Here are a few things you can do: 


Internal Testing 

Using representative datasets from clinical practices, you can conduct internal testing. This helps you simulate real-world scenarios and evaluate AI solutions against predefined criteria.  


External Validation Studies 

Collaborate with external stakeholders, like research institutions and healthcare organizations. By participating in collaborative research projects, you can validate your AI methods across diverse patient populations and clinical settings. 


Peer Review 

This gives a glimpse of what other healthcare and AI experts think about your processes, allowing you to improve and adjust as necessary. 


5. Conduct continuous monitoring and innovation. 

This includes tracking the performances of AI tools. Once you’ve collected enough data, you can analyze its efficiency. Focus on KPIs like patient outcomes, workflow efficiency, and healthcare cost reduction to assess the impact of AI on clinical practices.  

By conducting accurate audits and reviews regularly, your organization can quickly identify and address gaps in your AI implementation strategies. Additionally, being on top of the results allows you to maintain compliance with regulatory requirements, apply the best security measures, and improve ethical policies.  



With almost two decades of experience, Strategic Systems has established a network of experts and professionals who can help you implement the latest AI tools and technologies. 

Through our efficient filtering and rigorous vetting processes, you can rest assured that we aim to do more than just fill your open roles. Instead, it’s our mission to provide you with skillful and talented individuals who can drive organizational success. 

Contact us today to learn more! 



  1. Siwicki, Bill. “86% of Healthcare Companies Use Some Form of AI.” Healthcare IT News, 19 May 2017, 
  2. “AI in Healthcare Market Is Forecasted to Reach USD 427.5 Billion by 2032, Growing at a CAGR of 44.0% Over the Forecast Period 2023 to 2032.” Globe Newswire, 10 Oct. 2023, 
  3. Omiye, Jesutofunmi, et al. “Large Language Models Propagate Race-based Medicine.” NPJ Digital Medicine, 20 Oct. 2023,