AI Doctor: The Rise of Artificial Intelligence in Healthcare - A Guide for Users, Buyers, Builders, and Investors

Cognitive robotics can integrate information from pre-operation medical records with real-time operating metrics to guide and enhance the precision of physicians’ instruments. By processing data from genuine surgical experiences, they’re able to provide new and improved insights and techniques. These kinds of improvements can improve patient outcomes and boost trust in AI throughout the surgery. Robotics can lead to a 21% reduction in length of stay.

AI-powered passive monitoring is taking off and has huge advantages over the traditional way of monitoring patients. The advantage of passive monitoring, as opposed to data collected from wearables, is that it doesn’t require patients or seniors to actively wear a device at all times. Used in a hospital setting, the tech reduces healthcare workers’ risk of exposure to COVID-19 by limiting their contact with patients and automating data collection for vital signs. Also, camera-based monitoring is unpopular for the simple reason that a lot of people don’t like being watched by a camera.

Pilots used to fly planes manually, but now they operate a dashboard with the help of computers. This has made flying safer and improved the industry. Healthcare can benefit from the same type of approach, with physicians practicing medicine with the help of data, dashboards, and AI. This will improve the quality of care they provide and make their jobs easier and more efficient

An algorithm that expedites care to a stroke patient in a chaotic emergency room (ER) has a good chance of adoption. An algorithm that reads a routine scan and provides some quantification of what the physicians can already estimate won’t be in as much demand. There are good reasons for algorithms to parse patient records to look for signs of rare diseases, but there are fewer good reasons for using them to evaluate clinical symptoms. It’s cool that AI tools can make diagnoses from scratch, but for most clinical encounters doctors are already pretty good at it.

It’s true that AI can mimic the human brain, but it can also outperform us mere humans by discovering complex patterns that no human being could ever process and identify.

In short, physicians are getting more and more data, which requires more sophisticated interpretation and which takes more time. AI is the solution, enhancing every stage of patient care from research and discovery to diagnosis and therapy selection. As a result, clinical practice will become more efficient, convenient, personalized, and effective.

It’s estimated that AI could free up to 25% of clinician time across different specialties. This increased amount of time could mean less hurried encounters and more humane interactions, including more empathy from happier doctors. This is important because empathy has been shown to improve outcomes by boosting patient adherence to the prescribed treatments, increasing motivation, and reducing anxiety and stress.

The issue of reimbursement by payers is an important factor that should be discussed. Is it possible that if radiologists use AI to read scans, they’ll receive less reimbursement? Or to approach this from the other angle, if payers are reimbursing for the use of AI, will they pay radiologists less as a result? My discussions with insurance executives have shown that they don’t think this is likely. If the use of these technologies will improve patient outcomes and lead to fewer errors, there are benefits to them that will motivate executives to pay for them in addition to radiologists’ reading fees.

Used in combination with genomics, AI could help pharma companies to develop new drugs for rare diseases. The rarer a disease is, the smaller the market is and so the less likely it is to have been addressed. Big pharma is hesitant to take on the high development costs for new drugs if there’s no sign of a return on investment. Biological processes are complex, and that means that they lead to multidimensional data that human beings struggle to wrap their heads around. The good news is that AI is the perfect tool to spot patterns in this kind of data.

Much of clinician burnout is due to spending time writing notes, placing orders, generating referrals, writing prior authorization letters, and creating patient communication. In other words, burnout is caused by physicians having to generate output! With the emergence of large language models that are used to train generative AI solutions, these use cases will be at the frontier of AI’s applications in healthcare.