Artificial Intelligence in Clinical Decision Making: Supporting Health Professionals Without Replacing Their Judgement

A patient enters the consulting room with symptoms that do not fit neatly into a textbook description. The laboratory results suggest one possibility, the medical history suggests another, and the patient’s account adds something neither fully explains. This is where clinical judgement matters. Healthcare professionals must interpret evidence, recognise uncertainty, and decide what is appropriate for the person before them. Artificial intelligence may contribute to that process, but its recommendations should remain open to questioning.

The attraction of artificial intelligence in healthcare is understandable. Health professionals face demanding workloads and must make decisions using information spread across clinical notes, laboratory reports, medication records, and diagnostic images. Carefully developed systems could help organise this information, identify patterns, and draw attention to findings that deserve closer examination. The World Health Organization recognises these possibilities while emphasising that ethics and human rights must guide the design and use of such technologies (WHO, 2021).

My position is that artificial intelligence should strengthen the ability of health professionals to make informed decisions. Its value should be judged by whether it helps them provide safer and more appropriate care. A system that produces an impressive recommendation but leaves the clinician unable to assess its relevance has not adequately served the clinical encounter.

Clinical decisions involve more than identifying the most likely diagnosis. They also require consideration of treatment options, patient preferences, available resources, and the consequences of acting or waiting. Consider a hypothetical patient whose symptoms prompt a digital system to recommend further investigations. The clinician must still establish whether those investigations are necessary, whether the patient can access them, and how urgently they should be arranged. A recommendation becomes useful only when someone can translate it into appropriate care.

Patients also bring information that may never appear clearly in an electronic record. A person may struggle to explain symptoms, hesitate to disclose medication use, or avoid discussing financial difficulties. A careful conversation can reveal why a treatment plan is unlikely to work. Clinical judgement includes attending to these circumstances and adapting care accordingly. Digital information should help that conversation, while leaving sufficient time and attention for it.

However, defending professional judgement should not mean treating clinicians as infallible. Health professionals can overlook findings, become tired, or remain attached to an initial diagnosis despite emerging evidence. A useful AI system should be able to challenge their assumptions. Human oversight becomes meaningful when clinicians examine recommendations seriously, compare them with other evidence, and explain why they accept or reject them. Simply approving every output would offer little protection.

This makes education essential. Training should go beyond teaching staff which buttons to press. Doctors, nurses, pharmacists, and allied health professionals need to understand the intended purpose of a system, the information it requires, and the circumstances in which its output may be unreliable. They should also feel able to question a recommendation without being treated as resistant to innovation. Professional confidence must include the confidence to recognise uncertainty.

Hospitals have responsibilities of their own. It is unreasonable to introduce a poorly assessed system and expect individual clinicians to carry the entire burden of safe use. Institutions should define who reviews recommendations, how errors are reported, and when a system should be restricted or withdrawn. The WHO’s guidance places responsibility, accountability, transparency, and safety among the central principles for health AI. These principles should shape procurement and everyday practice (WHO, 2023).

In Ghana, I would argue for an approach grounded in the realities of the facilities expected to use these tools. Before adopting a system, decision makers should ask whether it has been assessed with relevant patients, whether the required information is available, and whether staff can act on its recommendations. A tool designed around investigations or medicines that a facility cannot provide may create additional work without resolving the patient’s problem.

The quality of health information deserves equal attention. Hospitals should examine how records are completed, how information moves between departments, and how missing or conflicting entries are handled. Purchasing sophisticated software should not become a substitute for improving these foundations. Investment in health informatics staff, reliable infrastructure, and clear documentation practices is part of preparing a service to use AI responsibly.

Patients must also remain participants in decisions about their care. Where an AI recommendation materially influences a clinical decision, they should receive an understandable explanation of its role and have an opportunity to ask questions. Confidentiality matters throughout this process. The WHO links human autonomy in health AI with control over medical decisions, protection of privacy, and valid informed consent (WHO, 2021).

The test of a clinical AI system should be its contribution to care. Does it help professionals recognise a problem earlier, make a better decision, or spend more useful time with a patient? Does it create unnecessary investigations, confusing alerts, or additional administrative demands? These questions deserve careful evaluation after implementation as well as before adoption.

Artificial intelligence deserves a place in clinical practice when its purpose is clear, its performance is examined, and its recommendations can be challenged. Health professionals should welcome assistance that improves their work while continuing to exercise judgement and accept responsibility for their decisions. Patients deserve clinicians who can explain what they recommend, acknowledge what remains uncertain, and listen when the person’s experience does not fit the screen.

References

• World Health Organization. (2021). Ethics and governance of artificial intelligence for health. World Health Organization.Source
• World Health Organization. (2023, May 16). WHO calls for safe and ethical AI for health.Source

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