What unexpected efficiency gain have you experienced from using AI in diagnostics? How has this impacted your patient care or practice management?

I am a board certified dermatologist, fellowship trained laser and Mohs surgeon, and Associate Clinical Professor of Dermatology at Mount Sinai. One unexpected gain from AI in diagnostics has been speed at the front end. It helps me sort lesion photos, frame the differential faster, and focus my attention on the outliers that need my judgment most. In a recent meta analysis of 38 studies, AI showed pooled melanoma sensitivity of 0.86 and specificity of 0.94, which mirrors why it works best as a sharp second set of eyes, not a replacement for mine. In practice, that has changed my day in a very human way. I spend less time on routine image sorting and more time talking with patients about biopsy decisions, scar planning, and what comes next. My visits feel calmer. Triage is tighter. Staff workflow is smoother. The real benefit is not just efficiency. It is better attention at the moments that matter most.

The efficiency gain I did not fully anticipate was in the pre-consultation stage rather than in the consultation itself. AI-assisted image analysis of retinal photographs and OCT scans means that by the time I sit down with a patient, the quantitative layer of interpretation is already structured. I am entering a clinical conversation with a framework already in place, which allows the consultation time to focus on what matters most: the patient's experience, their questions, and the nuances that no algorithm currently captures. The impact on practice management has been subtle but real. Clinics run more efficiently when the cognitive load of initial data interpretation is partially removed. This allows me to see complex cases with greater depth of attention because the routine quantitative work is handled upstream. However, there is always the risk of over-reliance. AI in check is a tool for augmenting clinical judgement. The cases where AI and clinical assessment diverge are exactly the cases that requires the most careful human attention.

One unexpected efficiency gain I've experienced from AI in diagnostics is how much time it saves in organizing and prioritizing information before I even make the final clinical decision. AI does not replace my judgment, but it helps by quickly structuring histories, highlighting key red flags, summarizing lab trends, and identifying patterns that might otherwise take longer to assemble manually. In a busy clinical setting, that reduction in cognitive load is extremely valuable. What surprised me most is that the benefit is not only speed, but also consistency. AI-assisted tools can help ensure that important details are not overlooked, especially when reviewing large volumes of data such as imaging reports, laboratory results, prior notes, or symptom questionnaires. In telehealth and outpatient practice, this is particularly useful because patients often provide fragmented or incomplete information. Having that information organized in a more usable format allows me to focus faster on the most clinically relevant questions. The impact on patient care has been meaningful. I can spend less time on administrative synthesis and more time actually talking with the patient, explaining findings, and discussing next steps. That improves communication, which is often just as important as the diagnosis itself. Patients feel heard, and consultations become more focused and efficient. From a practice-management perspective, AI has also improved workflow by helping reduce delays, streamline documentation, and support triage. That means better time allocation across the day and less mental fatigue for the clinician. In my view, the greatest value of AI in diagnostics is not that it makes medicine automatic, but that it gives physicians more time and clarity to practice medicine thoughtfully. Dr. Martina Ambardjieva, MD, Urologist Medical expert for Invigor Medical https://invigormedical.com/

One unexpected efficiency gain from AI in diagnostics has been the speed at which patterns can be identified across patient histories. In many clinics, reviewing years of notes, lab results, medication changes, and symptom reports used to take a significant amount of time before an appointment. AI supported chart review tools can now scan that information in seconds and highlight possible connections that might otherwise take much longer to notice. Instead of spending the first part of a visit sorting through records, providers can walk into the appointment already aware of trends such as recurring sleep issues, medication side effects, or patterns in mood changes. That shift has a real impact on patient care because more of the appointment can be spent listening and responding rather than reviewing paperwork. At Davila's Clinic, tools that summarize patient records or flag possible concerns allow clinicians to focus their attention where it belongs, which is the conversation happening in the room. Practice management benefits as well. Documentation time after appointments can be reduced, which helps prevent provider fatigue and keeps schedules running more smoothly. Patients often notice the difference when their provider already understands the background of their situation and can move quickly into meaningful discussion about next steps.

One unexpected efficiency gain from using AI in diagnostics has been the ability to identify patterns and prioritize cases much faster than traditional manual review. AI-assisted tools can quickly analyze imaging, lab data, or clinical records and flag potential abnormalities, allowing clinicians to focus their attention on higher-risk cases earlier in the process. This reduces time spent on routine data review and helps streamline diagnostic workflows. In practice, this efficiency improves both patient care and clinical operations. Faster preliminary insights allow providers to make timely decisions, shorten turnaround times, and improve communication with patients about next steps. It also highlights the growing need for healthcare professionals to be trained in working alongside AI-supported diagnostic systems, ensuring that technology enhances clinical judgment rather than replacing it.

One unexpected efficiency gain I saw from using AI in diagnostics was how much time it saved during the early review of patient information. Before, going through notes, lab results, and past reports could take quite a while, especially on busy days. With AI helping organize and highlight key findings, that first review became much faster. A good example was when reviewing imaging reports and lab patterns for patients with recurring symptoms. The system would quickly point out patterns that might not be obvious at first glance, such as changes over time in certain test results. Instead of spending extra time searching through past records, the important details were already summarized. What surprised me most was how this improved conversations with patients. Because less time was spent digging through data, more time could be spent actually talking with them about their concerns and explaining the results in a clear way. It also helped with practice management. Appointments stayed closer to schedule, and the team felt less rushed during the day. In the end the biggest benefit was not just speed. It was having a little more breathing room to focus on patient care rather than paperwork.

The most unexpected efficiency gain from AI in diagnostics has been the dramatic reduction in time spent reviewing normal results. AI triage systems that flag abnormal findings first allow clinicians to focus their attention where it matters most rather than working through a uniform queue. In practice this has meant that critical findings receive attention within minutes rather than hours. The impact on practice management has been significant as it allows the same clinical team to handle higher patient volumes without sacrificing accuracy. Patient care has improved because early identification of concerning patterns enables faster intervention conversations with patients. The surprise was how much cognitive load reduction translated into better clinical decision-making for complex cases.

At ClientCare.pro, we built AI to "diagnose" claims risk in home health the same way a clinician runs diagnostics before treatment — and the unexpected efficiency gain was consistency, not just speed. Our platform uses Gemini 2.0 Flash to analyze patient rosters and surface coverage lapses, OIG exclusion risks, and PDGM billing code gaps before a claim is ever submitted. What surprised us was that a 150-patient census that previously took a compliance coordinator 3-4 hours per week to manually audit now gets reviewed in under 90 seconds — with zero missed patients. The practice management impact has been measurable: home health agencies using our platform recover an average of $16,000-$20,000 per year per 100 patients in revenue they were leaving uncollected, primarily because AI could identify PDGM comorbidity coding mismatches at a scale and consistency no human reviewer could sustain. The most surprising efficiency gain wasn't the speed — it was the elimination of "Friday afternoon errors." AI doesn't have bad days, doesn't skip a patient because a roster is long, and doesn't misread a payer ID. For home health agencies managing Medicare and Medicaid claims across hundreds of patients simultaneously, that consistency turns out to be worth more than the time savings. We've essentially moved the "diagnostic moment" from after a claim denial (60-90 days after care delivery) to before care begins — which is where it should have always been.

One unanticipated advantage of implementing Artificial Intelligence (AI) technologies for Diagnosis has been the reduction of time consumed prior to the start of the formal Review process. Rather than having to manually screen through hundreds or thousands of pictures, labs or documents, AI provides a means to identify abnormalities, prioritize cases, identify the most pertinent information related to a case prior to interpretation, effectively transforming the Work Process from not simply being about a quicker way to interpret diagnostic examinations to a means of minimizing both the Administrative and Cognitive burdens associated with performing diagnostic studies. With respect to patient care as well as Practice Management, the effects are substantial. The benefit to patients is that through quicker identifying Priority Methodology, shorter wait periods between identification of the need for further testing and notification of the healthcare provider or provider's office, and quicker response times for Urgent cases. The benefit for the Healthcare Provider is improved ability to schedule patients, decrease bottlenecks in patient care and allow Physicians/Healthcare providers more time to make informed decisions and communicate with their patients. The key to a successful integration of AI into the workflow of Practice Management and Patient Care is to integrate the use of AI into the Process for Triaging patients, Prioritizing patient care and improving operational efficiency while providing the best level of service to the patients being treated.

The artificial intelligence (AI) industry's newest efficiency improvement has produced a new type of efficiency improvement: in addition to the speed of analysis, there is also less time wasted on operational and administrative workflow bottlenecks associated with daily work. AI can provide assistance by triaging cases, formatting reports, and identifying cases that should be examined more closely; therefore helping the clinician spend less time on repetitive reporting functions while increasing their focus on the higher-risk patients. Workflow consistency represents the greatest impact on both patient care and practice administration, rather than replacing a clinician's ability to think. When AI is integrated into systems such as PACS, EHRs, or reporting systems, the result is a reduction in the time it takes to produce a report, an increase in the ability to prioritise cases that require urgent attention, and the ability for all reports to be created in the same way. There are many examples of practice or operational efficiency benchmarked at 10% to 30% improvement for that specific practice.

An unexpected efficiency gain I've experienced from using AI in diagnostics is how quickly it flags patterns that would normally take much longer to catch manually. In my day-to-day work helping customers choose the right dumpster, I started using simple AI tools to analyze past orders and project types, and it immediately highlighted trends I hadn't noticed—like how certain home renovation projects consistently underestimated container size. That same idea translates to diagnostics: AI surfaces patterns early, which reduces back-and-forth and guesswork. One real example was when I used AI to review customer inputs and it suggested a larger bin size based on similar past jobs; the customer avoided an extra haul, saving both time and cost. That kind of predictive insight is similar to catching issues earlier in a clinical setting, improving outcomes by acting sooner. It's also streamlined my workflow—I spend less time double-checking and more time actually helping customers. Overall, it's made decision-making faster and more accurate, which improves both service quality and operational efficiency.