AI In Medicine: Transforming healthcare practices

AI in medicine is transforming the healthcare field, offering new possibilities like interpreting medical images, predicting disease outcomes and creating personalised treatment plans. There are countless possibilities and care providers recognise its potential in shaping the future.

An AI in Healthcare report by Forrester found that 60% of leaders in U.S. healthcare organisations said their company is not moving quickly enough to adopt AI and 86% said using data, technology and AI effectively will define their success over the next five years.

The way that healthcare leaders implement AI within their organisations could be a major contributor to their success. Let's look at the AI capabilities healthcare practitioners are so excited about as well as the challenges the new technology brings.

In this article you will learn:
What is AI in medicine?
Key applications of AI in medicine
The benefits of AI in medicine
The impact of AI on patient care and outcomes
Ethical considerations and challenges in AI Healthcare
The future of AI in medicine

AI in medicine uses technologies like machine learning, natural language processing, computer vision and neural networks to improve healthcare processes. These tools help with tasks such as data analysis, diagnosis, treatment, patient monitoring, administration and research, saving time for both providers and patients.

AI has a wide range of applications in medicine, transforming how healthcare is delivered in several ways.

• Early detection: At hospitals and clinics, AI can analyse hundreds of images from patient X-rays and other scans to detect abnormalities — and use editing tools to highlight them for doctors to review.
• Intelligent assistance: In operating theatres, cameras equipped with AI computer vision can assist doctors with surgeries in real time. Some operations can even be performed partially or entirely by doctor-guided robotic arms.
• Predictive analytics: Another powerful feature of medical AI is its ability to analyse a patient's medical history and predict outcomes based on specific risk factors. By reviewing images, lab results and doctor's notes, it can identify potential risks for disease and track a patient's health progress, offering early warnings and personalised insights that can lead to better outcomes.
• Data analysis: AI also plays a big role in medical research. It can comb through the data of thousands of patients and discover disease patterns, helping doctors make better treatment decisions and speeding the progress of new therapies. For example, a predictive AI model identified which patients would likely benefit from a therapy for prostate cancer.

Doctors and nurses spend an enormous amount of time on documentation and administrative tasks, reducing the time they can spend with patients. This not only contributes to decreased patient engagement, it can drive burnout and raise costs for organisations.

Administrative costs account for 15% of U.S. healthcare spending, presenting a potential cost savings of up to $570 billion that could be spent serving patients and improving care. AI is helping organisations become more efficient and agile. For example, AI can transcribe and summarise patient information quickly — even analyse test results and make diagnostic and treatment recommendations — which saves medical staff time and improves accuracy.

Paired with an AI-powered healthcare CRM, organisations can greatly improve misdirected administrative spending while improving patient engagement.

Autonomous AI agents are another emerging AI application with promising benefits. Some of the ways this technology can support healthcare organisations include:

• Optimised workflows: Every healthcare organisation has processes that aren't as efficient as they could be. Creating customised AI agents is one way to change that. Autonomous agents can quickly learn your procedures and perform specific tasks. For example, they can answer patients' questions 24/7, review their insurance coverage and help them schedule appointments. Or take over tedious tasks, like arranging prior authorisations.
• Efficient operations: Accounting, supply chain, HR, IT and facilities management can all benefit from AI. You don't have to be a coder to put AI agents to work — just type out what you want them to do, give them the resources they need and they learn to do it.
• Data control and privacy: AI agents can be built with guardrails strictly defining what they can and can't do. This includes always following your organisation's security and compliance rules and sticking to their narrowly defined roles.

In addition to time and cost savings, healthcare organisations can use AI to improve patient care in the following ways:

• Fewer errors: Manual processing can sometimes lead to unfortunate mistakes. In medicine, that's a big deal, because a simple mistake — like entering an incorrect dosage or prescription — can have drastic consequences for patients.
• Higher accuracy: By directing doctors' attention to abnormal results, AI increases diagnostic accuracy — in some cases sparing patients from procedures and tests they may not need.
• Smoother communication: Patients often work with multiple caregivers. This can lead to confusion about who does what and where to turn for help. An AI-powered CRM like Health Cloud can proactively co-ordinate care teams and alert the right people when an intervention is needed, no matter who the patient contacts. Prompt responses can improve patient outcomes and reduce hospital readmissions.
• Personalised care: Patients value personalised help and are more likely to stay engaged and follow regimens when they get it. Health Cloud serves as a hub for many AI services tailored to individuals, including telehealth apps, remote patient monitoring devices, appointment reminders and tips for self-care. These can go a long way towards improving outcomes.

For all its benefits, medical AI raises concerns about security, privacy and ethics. And because the content it's built on was created by humans, bias may affect results.

To safeguard the sensitive medical data used to train AI models, organisations need robust security measures, including encryption, access controls and regular audits to make sure security protocols are followed. When using medical AI, it's critical to keep humans in the loop to check for issues like bias, accountability and potential job displacement.

To build or use AI models responsibly, create guidelines to address all these concerns. Some organisations like Salesforce are already doing that by building a security layer into their AI platforms.

A good place to learn more about AI gatekeeping is the National Institute of Standards and Technology (NIST), which worked with private sector and government organisations to develop a comprehensive AI risk management framework.

In just a short time, medical AI has made significant progress. Not only can it interpret complex medical images and sort through vast volumes of patient data to predict outcomes, but it can also co-ordinate care and provide patients with personalised treatment plans — among other applications.

As it turns out, AI and medicine are a great fit. But today's solutions aren't perfect. We still have a long way to go in certain areas, namely, security, privacy and ethics, where developments are still evolving.

While leaders continue to determine the best ways to implement AI in ways that benefit healthcare organisations and patients, the existing benefits are irrefutable. AI tools and technology are already saving healthcare organisations time, improving treatment outcomes and enhancing healthcare professional and patient experiences every day.