Artificial Intelligence (AI) in healthcare is no longer a thing of the future, but is already revolutionising the sector. From more accurate disease diagnosis to enhanced hospital management, AI is making medical care smarter and more accessible. But AI implementation in healthcare is not as easy as implementing new software. It involves planning, ethical implications, and smooth integration with current systems.

For policymakers, hospitals, and healthcare professionals, the actual challenge is not whether to implement AI but how to do it effectively. Well-implemented AI systems can improve medical outcomes and improve patient care. However, if not done correctly, AI integration can lead to problems of data security, biased outcomes, patient privacy violations, and workflow disruptions.

How, then, can AI be effectively used in healthcare? Here’s a step-by-step guide to implementing the adoption of AI in a manner that benefits all stakeholders involved.

Step 1: Identify key areas for AI integration

It is critical to ascertain some areas that can benefit from AI the most prior to venturing into AI adoption. There are several fields within the healthcare sector where AI has the potential to add value. Some areas in which AI is commonly used are as follows:

• Studying X-rays, MRIs, and CT scans for the early and accurate detection of diseases such as cancer, tuberculosis, and neurological conditions.
• Evaluation of patient information in order to predict possible health issues so that patients can seek treatment early on.
• Tailoring medications and treatment methods according to a patient’s genetics and medical history.
• Managing patient records, doctor schedules and billing, freeing hospitals from paperwork.
• Monitoring patient vital signs in real time via wearables, eliminating the necessity for repeat hospital visits.

Step 2: Ensure high-quality data collection

AI is only as good as the data it was trained on. Low-quality data can result in incorrect diagnosis, biased treatment, and unstable predictions. For example, if an AI model was trained mainly on urban patient data, it might perform poorly when used in rural healthcare systems. Having clean, structured, and diverse datasets is important to the success of AI in healthcare. Some of the best practices for data collection are:

• Use electronic health records (EHRs) for storing patient data.
• Standardise medical data formats between hospitals and clinics.
• Ensure the diversity and robustness of data to avoid biased outcomes.
• Anonymise patient information to safeguard privacy.

Step 3: Select the right tools

Healthcare organisations have varying requirements, and the selected AI tools must be well-suited for specific objectives. It is important to select reliable and well-tested solutions to facilitate easy deployment and to ensure regulatory compliance. The various types of AI tools used for healthcare applications are:

• Machine learning (ML) models are applied for predictive analytics, diagnosis of diseases, and personalised treatment suggestions.
• Natural language processing enables AI to understand medical reports and scans.
• Robotic process automation, which automates routine administrative process work such as billing, appointment scheduling, and patient record keeping.
• AI-driven chatbots help support appointment scheduling, answering FAQs, and conducting remote symptom checks.

Step 4: Train your teams on AI

AI is not here to replace medical professionals. Effective AI implementation, however, needs healthcare professionals to be educated on how to employ these tools in their line of work. Here’s how you can get medical professionals up-to-speed when it comes to AI implementation:

• Conduct workshops, webinars and training sessions.
• Offer practical experience with AI-based diagnostic equipment.
• Educate on interpreting AI-derived insights instead of following AI decisions blindly.
• Clarify job replacement fears by demonstrating how AI complements, not replaces, human skills.

Step 5: Pilot test the system before large-scale deployment

Prior to rolling out AI across a whole hospital or healthcare system, it’s important to pilot the system in real-world environments. Through pilot tests, you can reduce risks, enhance system accuracy, and make the transition smoother. What you need to do when you roll out a pilot is:

• Roll out AI in one department first before hospital-wide.
• Compare AI diagnoses with doctor diagnoses for accuracy.
• Gather feedback from patients and from your team regarding the ease-of-use of the system and output accuracy.
• Incorporate the feedback that you have received prior to moving ahead with large-scale deployment.

Step 6: Improve the system continuously

AI must be approached as a ‘living’ system that adapts to the latest developments in the field of medicine, and not as a one-time installation. Models must be retrained using the latest data to stay updated with the latest medical trends. Here are some best practices that you must implement to improve the AI systems in your organisation:

• Evaluate AI-generated diagnosis and treatment recommendations against real-world outcomes.
• Gather user input from patients to enhance system usability.
• Ensure that systems are continually updated to stay in line with regulations.

Conclusion

Effectively implementing AI in healthcare requires planning and refinement. From the detection of early diseases and predictive analytics to robotic surgeries and administrative automation, AI can significantly improve healthcare.

In India, NBFCs (Non Banking Financial Companies) are at the forefront of funding AI-based healthcare startups and hospital infrastructure. AI is also transforming the online marketplace for healthcare, allowing patients to schedule doctor consultations, procure medicines, and avail virtual healthcare support with ease.

By effectively using AI, healthcare institutions can improve the care of patients, make operations smoother, and welcome an era where medical treatment is faster, smarter, and more accessible than ever.