Searching for physicians, like other sectors, now relies on user-generated material on web sites. Advances in communication technologies improve internet access and information exchange.

Patients are already sharing their experiences on online sites that are popular for travel, education, and health information. 7% of internet searches are for health-related information, and 3/4 of individuals use online reviews to choose providers.

Reviews frequently include ratings, which help other patients. Understanding and forecasting these scores, however, becomes difficult due to subjectivity and affecting factors.

High hospital ratings, for example, may imply cost or fast wait times, catering to certain desires. However, they may not benefit people who prioritize doctor experience or treatment quality.

Analytical methods and natural language processing unveil insights from reviews, like the “Bag-of-words” approach. While simple, it overlooks text nuances, risking irrelevant recommendations and eroding patient trust.

Deep learning, a subset of AI, excels in predictive tasks, surpassing conventional methods. It’s embraced across fields like agriculture, finance, robotics, and healthcare. In research, a deep learning model using Recurrent Neural Networks and an attention mechanism effectively predicts online doctor ratings.

The usage of almost 35,000 reviews and ratings outperformed previous approaches. This technique has the potential to turn rating websites into accurate recommendation engines, benefiting both patients and the general public.

Aligning with India’s Digital Vision, leveraging e-health records and public reviews can personalize doctor recommendations via deep learning, enhancing healthcare decisions.