The question today is no longer whether artificial intelligence can diagnose diseases with the same accuracy as a doctor. Hundreds of studies in recent years have demonstrated that many intelligent systems are now competing with experts, and sometimes even surpassing them in specific tasks, such as interpreting medical images and detecting certain diseases in their early stages.

But as these technologies become more prevalent in clinics and hospitals, a more important question has emerged: If the algorithm is correct, will the doctor trust it?

The answer may seem obvious, but the reality is more complex. The success of artificial intelligence in healthcare depends not only on the accuracy of the algorithm but also on the physician's confidence in its recommendations, the patient's trust in its use, and the existence of a clear system of governance, transparency, and accountability. Without these elements, even the most advanced systems, no matter how efficient, may remain outside of clinical practice.

Three

recent studies from Saudi Arabia and the United Arab Emirates reveal that the biggest challenge to adopting artificial intelligence in Arab medicine is no longer technical but rather related to building trust. The real obstacles lie in training, the transparency of the systems, the governance of their use, and the determination of responsibility when errors occur. Each of these studies presents a different part of the picture, starting with the physician, moving through the patient, and ending with the entire healthcare system.

The first study: Physicians aren't solely focused on accuracy.

In April 2026, the journal JMIR AI published a study led by Emirati researcher Ghufran Al-Saloum, which included 182 physicians, dentists, and nurses, to explore the factors influencing the adoption of artificial intelligence (AI) applications in healthcare practice.

The results showed that trust was the most influential factor in the decision to use AI, even surpassing ease of use and the system's technical performance. Even when a physician is convinced of the accuracy of the results, they may hesitate to rely on them if they cannot understand how the algorithm arrives at its recommendations, or if legal and ethical responsibility remains unclear in the event of an error.

The researchers concluded that the success of AI in healthcare depends not only on developing more powerful algorithms but also on building an integrated system based on explainability, transparency, and clear governance that fosters trust among healthcare practitioners.

The second study: Saudi society welcomes AI, but with caution

. In Saudi Arabia, the question has shifted from physicians to patients. On March 25, 2026, the International Journal of General Medicine published a study led by Dr. Yusra Al-Hindi from Umm Al-Qura University, titled "Saudi Society's Knowledge and Acceptance of AI-Supported Physicians," which included 303 participants from various regions of the Kingdom.

The study revealed that Saudi society views the role of artificial intelligence (AI) positively in improving diagnosis and accelerating access to healthcare, but does not grant these technologies unconditional trust. Participants' acceptance was linked to the availability of three essential elements: transparency in the systems' operation, protection of health data privacy, and clear accountability in case of errors.

These findings align with those of the Emirati study; the issue is no longer about AI's ability to provide the correct answer, but rather its ability to gain the trust of the people who will rely on it.

The third study indicates that the problem begins before the hospital

, but building trust doesn't start with the doctor or the patient; it begins much earlier, with the readiness of the healthcare system itself. In June 2025, the journal *Patient Preference and Adherence* published a study led by Dr. Safaa Al-Sanousi from Umm Al-Qura University, which addressed the role of artificial intelligence in supporting treatment adherence among patients with chronic diseases.