Artificial Intelligence is reshaping industries worldwide, and healthcare is a huge part of the mix. As an emerging technology for patient care, AI tools are elevating diagnosis, treatment, and patient experience1. From predictive analytics that anticipate diseases to virtual health assistants guiding patients, AI's impact on healthcare is profound and promising2.

In the following article, we explore some of the top AI tools transforming patient care and making a tangible difference in real-time health outcomes12.

Perhaps one of the biggest impacts of AI in health so far has come in diagnostic imaging and radiology – where clinicians have to review thousands of images a day, from x-rays to MRIs, and with machine learning-powered algorithms able to analyze them with the same precision as a clinician's eye, but without fatigue12. The Zebra Medical Vision and Aidoc automated tools are already gaining use in machine learning to spot anomalies and, potentially, issues that a clinician's eye might miss34. These tools increase diagnostic precision and speed, enabling us to detect cancers, fractures, or neurological disorders while they are still amenable to early and effective treatment12. Notably, the AI startup Aidoc has developed algorithms that flag life-threatening cases, empowering radiologists to operate more efficiently by first addressing patients most in need of urgent care45. Given a large database of medical images as a training set, the AI model continually enhances its accuracy and reduces diagnostic error – ultimately improving outcomes for patients126.

Predictive analytics is another area of AI innovation entering health care by making use of data from electronic medical records, wearable devices, and other health metrics to predict when individuals may be at risk of disease well before symptoms appear. In these settings, prediction becomes prognostication, and even early detection can reduce progression and improve patient outcomes12. For example, IBM Watson Health uses artificial intelligence and machine learning to review monumental amounts of health information and predict outcomes. Given information about a patient, the tool can help medical professionals predict when a person might develop heart disease or diabetes, and then create a plan of care for them34. Predictive analytics in health has the potential to perform individual treatment as well as provide an understanding of health trends of populations, to provide care for groups of people more effectively and preventatively56.

Virtual health assistants (VHA) are digital, AI-powered assistants working in a one-to-one relationship with patients, assisting with their health life administration. VHAs, like those created by offerings from Babylon Health and HealthTap, answer health-related questions, remind patient-users to take medications, or provide guidance for minor symptoms12. This can relieve the pressure on doctors' surgeries and provide immediately available answers to patients' concerns. Some health assistants, such as the apps from the company Sensely, utilize interfaces that feature avatars, making interaction with patients more pleasant and friendly2. These avatars can log symptoms, oversee the management of chronic disease, and provide care guidance, based on patient replies. These virtual helpers are especially beneficial for patients with chronic illnesses, as they get prompts and reminders regularly to help them stick to treatment protocols12.

Another burgeoning medical field where AI is being used to usher in genomic-scale precision care is genomics, the study of an individual's DNA. A patient's genes can provide crucial insights into how they might be predisposed to specific illnesses and how they will respond to various treatments. AI-powered tools such as Deep Genomics and Google's DeepVariant enable the analysis of genetic data to predict the emergence of disease, and suggest the most appropriate treatment or prevention methods, given an individual's unique genomic profile.12 This helps identify those mutations and genetic markers connected with the disease in efforts to enable the tailoring of medical treatments to the individual. For example, the Canadian start-up Deep Genomics uses a kind of machine learning to automate the analysis of genetic mutations to reveal the impact on proteins. This, in turn, offers crucial insight into complex genetic disorders, ultimately helping doctors design more effective therapy for patients.3 This idea of 'personalizing' medicine to the individual can make treatments more successful and play a crucial role in reducing adverse reactions to therapy.45

In the same vein, robotic systems powered by artificial intelligence are increasingly designed to supplement or guide a surgeon either by tracking and quantifying precise movements relative to their objective or, in the case of physically interactive robotic surgical devices such as the da Vinci Surgical System, by actually guiding or performing the operation. AI (and robotics) in medicine, for example, assists surgeons in tasks that require exquisite levels of dexterity and precision, thus yielding improved outcomes and reduced patient recovery time and possible surgical risk12. These devices are physically interactive robotic surgical devices, where the surgeon is in control, but the device is enhanced with AI that enables the surgeon to track anatomy and provide a physically interactive surgical tool that can perform exquisitely precise motions3.

AI-enabled robotic surgery is less likely to under- or overtreat, allowing for fewer complications and errors overall than a regular, human-only surgery would allow4. For example, in orthopedic surgeries, AI-enabled robots can model how to implant a knee replacement so that a person would be able to walk sooner with less damage to the metal implant from the surrounding bone5. Additionally, because AI-enabled robotics are more consistent and less error-prone than many purely human-led techniques, it can train novice surgeons in a supervised fashion67.

Determining a customized treatment protocol poses a huge hurdle for modern medicine. With a complex system of treatment options and various co-occurring health data, AI tools make it possible to use accumulated research to present what works for each patient, based on a careful analysis of their own medical history and responses to available treatments. For example, IBM Watson for Oncology analyzes a patient's medical history, existing health data, and available treatment responses to help recommend an evidence-based treatment protocol. Oncologists can use the tool to confirm their recommendations or to flesh out a course of treatment.12

For example, AI-powered personalized treatment plans can make an especially big difference in oncology and chronic disease management, where a patient's response to a given treatment can vary widely from the next. PathAI, a computer-based tool used to analyze biopsy slides, can help detect characteristic patterns in tumors that enable physicians to tailor a cancer patient's therapy. By increasing the precision in diagnosis and treatment, new AI tools enable physicians to provide the right care to each patient at the right time.34

The process of developing new drugs is lengthy and costly, often taking several years. This is changing, thanks to AI. The technology is accelerating this process by enabling researchers to analyze vast datasets, identify potential drug candidates, and predict how effective they will be. Companies like Insilico Medicine and BenevolentAI use AI to scan millions of compounds and identify those with the highest potential for treating specific conditions. During the COVID-19 pandemic, AI-assisted drug discovery played a very important role in identifying compounds that could be used (and even repurposed) to combat the virus. By cutting down on the time and staff needed to develop new drugs, AI can save numerous lives, especially when timing is everything.

Wearables and remote monitoring devices use artificial intelligence to empower patients to take responsibility for their health outside the hospital. Fitbits and Apple Watches use AI to remotely monitor vital signs such as heart rate, blood pressure, and sleep patterns. This benefits patients with chronic conditions who can be monitored by their primary healthcare provider for vitals, and, given an early diagnosis, receive quick and effective medical intervention. Wearables also allow patients to reclaim control over their health, significantly reducing the demand on healthcare systems to provide uninterrupted care for patients at hospitals. It also enables health workers to make smart and informed interventions.

In the field of clinical documentation, one branch of AI – natural language processing (NLP) – has the potential to save more time for doctors, who often struggle with lengthy paperwork at the expense of patient contact. Nuance's pioneering Dragon Medical software, for example, converts speech into written documentation, while the newer Dragon Medical One is a cloud-based desktop browser to both verify and finalize clinical documentation. NLP is also extracting more valuable data from clinicians' open-ended notes, which is essential for research and analytics. Simplifying data capture in clinical documentation reduces clinician workloads, while also improving the integrity of the medical record and leading to better-coordinated patient care.

There are AI chatbot platforms, such as Woebot (pictured above) and Wysa, that provide therapy without an actual person managing the effort. Users can confide in these online avatars, anonymously, which can provide an environment where people don't feel judged.12 Many users who talk to an online counselor report feeling relief. These programs can provide help based on cognitive behavioral therapy (CBT) – an effective and recognized mental health treatment – and help articulate coping strategies to users in distress. Woebot follows a user dialogue using natural language processing.13

MHWs can be particularly effective in underserved areas: one group, in southern Mauritania, found that the WhatsApp platform was 'a breakthrough in terms of geographical accessibility'. While these mental health tools might not fully replicate the benefits of seeing a human therapist, they can be an effective, practical tool for mitigating mental health symptoms or providing care in areas with limited access to mental health professionals.45

The AI solutions reshaping patient care today will grow more sophisticated in the years ahead. Predictive models are coming that can map an illness across populations to anticipate a pandemic. As consumer technologies advance, there will be wearable devices gathering dozens of health metrics all the time.

AI in healthcare represents an important shift towards a more proactive and personalized approach to patient care. By improving diagnostics, enhancing treatment precision, and making healthcare easier to access, AI is literally defining the future of healthcare as we know it.