The Role of AI in Modern Medicine: Opportunities and Challenges

AI offers a great asset to practitioners in the health arena, such as an improved precision in diagnosis, optimised patient care and telemedicine services. However, like with all the other aided technologies, we cannot forget that this tool is supposed to support people’s expertise, rather than replace it.

It can help medical staff manage their workloads and automate functions more efficiently, and make information immediately available. For insurers, AI can detect irregularities, such as unbundled services billed before they were denied for being performed. This can help both to save insurance claim denial costs and to save cabin fees for claim denial services.

Enhances Diagnostic Accuracy

AI can help us to pull, mine, sift and analyse million of data points, including medical records, exam findings and free-text narrative data to put the many pieces together to create a realistic multifaceted view of a patient. It has the power to reduce preventive care holes. With some limitations, AI can augment human expertise by reducing mistakes and improving patient care – but determiners of its final design are complex; validation is key to accuracy ensuring. Ultimately, AI should be well-understood, neutral and respectful of patient confidentiality. It also needs to work within existing healthcare infrastructures and fit with physicians’ workflows so that it can be successful.

Improves Patient Care

Patients can benefit directly from AI when a digital health assistant provides nudges and tailored wellness suggestions, or when they have telehealth consultations. AI can help to alleviate physician burnout, by automating that physician’s administrative workflow, freeing up more time for patient-facing activities. New medicines are expensive both to discover and to create; AI could cut through existing data sets to help doctors focus their work and, subsequently, quicken breakthroughs while substantially cutting costs. By choosing vendors who can sign off that they will develop systems that are ethical and non-discriminatory, and transparent in how their products will be used (which will certainly be critical in seeking optimal outcomes for patients), healthcare leaders can build a trustworthy AI environment.

Enhances Patient Education

AI can help patients better understand diagnosis and treatment options, track symptoms and encourage medication adherence. Some AI systems even translate medical documents into multiple languages and provide patient education materials at multiple reading levels. AI can be tremendously useful in boosting hospital efficiency in this way and in another area: preventing healthcare fraud. It can already detect bogus billing issues, such as unbundling (when a doctor, for example, bills for having performed a procedure that they didn’t) as well as upcoding (overcharging), as well as tests that doctors performed but didn’t actually analyse, and other inflated claims to get money from insurance companies. There are drawbacks to AI, too, that remain to be overcome with human presight: AI algorithms will, for instance, become biased if their data set is not sufficiently broad to include samples from minority groups.

Enhances Patient Safety

Thanks to artificial intelligence, chances are that the next advance in patient safety to come out of the healthcare system could be created by a student who is still at school. Looking for ill-defined patterns on CT scans goes beyond what a human doctor could spot in a short time. Furthermore, it can alert clinicians to patients who are at high risk of suicide and connect them with appropriate care; and it can detect physician and nurse burnout which can lead to error. However, AI does pose a few challenges in healthcare settings. For instance, we cannot define outcomes – the best performance criterion – for AI systems in a standardised way; and if AI makes an error, we wonder who should be held accountable – if a human makes an error while using AI then who should be punished or held liable?

Reduces Hospital Readmissions

By leveraging AI, healthcare groups can reduce readmission rates and redirect savings into improving patient care. If clinical and sociopersonal training data are available, AI tends to do better for predictive/grouping work, but can’t develop a personalised clinical management plan without the input of physicians and data scientists. quality of the training data dictates accuracy, so it becomes paramount that data acquisition takes precedence over all else, so as to prevent unintentional biases from driving social and health inequities downstream.

Enhances Telehealth

AI can improve doctor-patient contact: a doctor who avoids back-end work such as data input might spend more time with patients. If only specialists or programmers were involved in maintaining data, more people would have remote access to medical services. Telemedicine can also monitor vital signs and symptoms remotely, such as during heart attacks or strokes, as well as emergency care and triage. However, it needs to be monitored by physicians and aligned with established clinical practice; the associated benefits and risks, including those relating to privacy, need to be taken into account along with socioeconomic and other considerations; and it needs to be continually assessed and updated to address the problems that arise as it goes.